Any intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. These machines use mathematical models that identify and encode patterns in data sets, which can then perform predictions on new situations which they haven’t encountered before.
Why It Matters:
In 2023, AI matters significantly because it is rapidly integrating into our daily lives, revolutionizing industries, and altering the way we work, learn, and communicate. AI technologies facilitate data-driven decision-making, automate routine tasks, and contribute to unprecedented levels of efficiency and productivity. This swift technological evolution underscores the need to integrate AI education into curricula, not only to prepare students for academic success but also to meet workforce demands. Crucially, attention must be given to underrepresented populations, ensuring inclusivity in accessing and benefiting from the latest technological advancements.
Explore More:
Generative AI is a category of artificial intelligence models designed to create new, original content, such as text, images, audio, video, code or synthetic data, by learning patterns, structures, and relationships from large datasets. These models do not merely replicate existing data but generate novel outputs that resemble the training data in form and context. Key techniques include transformer-base models (e.g., GPT, DALL-E, Stable Diffusion). Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models.
Why it Matters:
Its ability to generate novel content, automate complex tasks, and optimize processes holds the potential to revolutionize industries, drive efficiency, and foster continuous advancements in technology and problem-solving. In essence, Generative AI stands as a cornerstone in pushing the boundaries of what's possible, making it a critical force in the evolution of AI applications across diverse fields.
Explore More:
ChatGPT (short for Chat Generative Pre-Trained Transformer) is a series of generative AI chatbots launched by OpenAI in November 2022. It is a Large Language Model that produces a body of unique text from a user’s specific input based on existing content from the internet. The latest version of ChatGPT is multimodal and can recognize images, generate images, engage in voice conversations, and search the internet in real-time through the same interface.
Why it Matters:
Trained on a vast 45-terabyte dataset, it excels in comprehending and generating human-like language. Its standout feature lies in consistently delivering coherent and contextually relevant responses to open-ended queries, making it a powerful and widely utilized tool.
Chat GPT (AI) Capabilities:
Explore More:
The user interface for most non-AI computer programs is a mouse, keyboard, or touch screen. Chatbots provide a different kind of user interface for AI systems, one that uses speech (either spoken or typed). These AI programs range in sophistication from relatively simple and rule-based (e.g., providing a canned response to a specific question) to more complex and AI-enabled (able to parse human language and learn from previous conversations to improve accuracy constantly).
Why It Matters:
It is easy to build a simple chatbot, but complex to build a genuine AI chatbot — which is why the arrival of ChatGPT has taken the world by storm. Because they can respond to a nearly limitless number of users at once, chatbots have the potential to provide real-time support at unprecedented scale — which, in the context of higher education, is helping institutions boost enrollment and student success while enabling advisors to focus on students who need more hands-on, personalized guidance.
A type of machine learning model that can perform a variety of natural language processing (NLP) tasks such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another.
Why it Matters:
Unlike smaller-scale language automation algorithms, LLMs, exemplified by models like GPT-3, have the potential to revolutionize communication by producing more sophisticated and human-like outputs. Their adaptability to tasks beyond explicit training sets them apart, showcasing their capability to transform how we communicate and engage with technology and information.
Explore More:
A subfield of AI that uses algorithms trained on data sets to create self-learning models that are capable of predicting outcomes and classifying information without human intervention.
Why It Matters:
Machine learning is used today for a wide range of commercial purposes, including suggesting products to consumers based on their past purchases, predicting stock market fluctuations, and translating text from one language to another.
Explore More:
Deep learning models are a subset of neural networks.With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. The decisions by deep learning models are often very difficult to interpret as there are so many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language).
Neural networks, known as artificial neural networks are information processing systems. They act as a series of machine learning algorithms that seek relations in data sets.
Neural networks essentially mimic the way human and animal brain works. They resemble the structures of interconnected neurons, which are nerve cells that send messages throughout the body. This extreme interconnectedness and rapid communication is what makes them so effective in processing information and learning to solve problems.
A set of instructions or computations that a machine follows in order to learn how to do a particular task. Machine learning algorithms can discover their own rules (see Machine learning for more) or be rule-based where human programmers give the rules.
A machine learning technique where a model trained for one task is adapted for a related task. Many generative AI models use transfer learning to leverage knowledge from large-scale pretraining before fine-tuning for specific educational applications.
The initial input text or instructions given to a model to generate new content based on that starting point. It provides context and guides the model's output. The prompt can be a few words or sentences that set the tone or specify the desired content.
Explore More:
Misinformation or made-up information based on a pattern that the AI model has learned as part of its training. For example, the model could create references that do not actually exist.
Explore More:
Prompt engineering in AI is the organized creation, improvement, and fine-tuning of instructions for Generative AI systems. It helps AI produce desired outcomes and promotes smooth communication between humans and AI. This practice involves continually assessing and categorizing instructions to keep them relevant and effective.
Explore More:
Wharton's School Crash Course: Practical AI for Instructors and Students
Part 1: Introduction to AI for Teachers and Students Part 2: Large Language Models (LLMs)
To view the entire 5-part crash course visit:
Wharton Interactive Crash Course: Practical AI for Instructors and Students