Discipline that focuses on using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
Examples: Face recognition, smart assistants (i.e., Siri and Alexa), GPS predictions
Subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Methods can be used supervised, semi-supervised, or unsupervised.
Example: Self-driving cars use deep learning models to automatically detect road signs and pedestrians
Use of operations, systems, and technologies that allow computers to process and respond to written and spoken language in a way that mirrors human ability. To do this, natural language processing (NLP) models must use computational linguistics, statistics, machine learning, and deep-learning models.
Examples: Email filters, search results, and predictive text
Type of model in NLP that predicts the next word or character in a sequence. These models are used in speech recognition, text generation, and other NLP tasks.
Examples: Speech recognition (e.g., Siri, Alexa), Machine Translation (e.g., Google Translate), Text Suggestion (e.g., Grammarly), and Text Suggestion (e.g., GPT-3)
Refers to the segments of text that are fed into and generated by the machine learning model. These can be individual characters, whole words, parts of words, or even larger chunks of text. You can think of tokens as the “letters” that make up the “words” and “sentences” that AI systems use to communicate.
Example:
A process in machine learning where a pre-trained model (e.g., GPT) is trained for specific tasks or use cases.
Examples: Adjusting AI chatbots for enhanced customer service experiences and customizing output characterizations for adjusting tone, personality, or level of detail.
A technique used in machine learning to automatically categorize text into predefined classes or categories.
Examples: Classifying short texts (i.e., tweets, headlines, chatbot queries, etc.) and organizing much larger documents (i.e., customer reviews, news articles, legal contracts, longform customer surveys, etc.)
Uses pre-trained models from one machine learning task or dataset to improve performance and generalizability on a related task or dataset.
Example: A model trained for autonomous car driving can be used for autonomous truck driving.
In the context of AI, a prompt is an input given to a language model that it uses to generate a response or output.
Examples:
Writing - Compose a persuasive essay arguing for the importance of protecting national parks.
Image - Design a book cover for a children's story about a family of cats.
Art - Generate a piece of artwork with cinematic lighting that shows an actor on a Broadway stage.
Education - Create an online quiz that tests students' understanding of the geography of Africa.
The process of feeding curated data to selected algorithms to help the system refine itself to produce accurate responses to queries.
Examples:
Healthcare - AI models are being used to diagnose diseases more accurately, develop new treatments, and personalize patient care.
Retail - AI models are being used to personalize product recommendations, improve customer service, and optimize inventory levels.
Transportation - AI models are being used to develop self-driving cars, optimize traffic flow, and predict demand for transportation services.
Occurs when there are inherent and erroneous assumptions in the machine learning process that skews the output, influencing or limiting the results we are offered.
Example: "With the dream of automating the recruiting process, Amazon started an AI project in 2014. Their project was solely based on reviewing job applicants’ resumes and rating applicants by using AI-powered algorithms so that recruiters don’t spend time on manual resume screen tasks. However, by 2015, Amazon realized that their new AI recruiting system was not rating candidates fairly and it showed bias against women.
Amazon had used historical data from the last 10-years to train their AI model. Historical data contained biases against women since there was a male dominance across the tech industry and men were forming 60% of Amazon’s employees."
