For those in the cannabis industry who are new to AI, here is a list of 20 foundational terms necessary to understand the basics of artificial intelligence. CannManage is developing an occupational training program that aims to assist the cannabis professional in optimizing their workflow using AI, and more details will be released next week. Stay subscribed to our newsletter for special discounts on upcoming classes.
Artificial Intelligence (AI): Refers to machines designed to emulate human cognitive functions. AI encompasses a broad spectrum of technologies, from basic algorithms like those in chess programs to complex systems like ChatGPT and Gemini.
Algorithms in AI: These are specific instructions guiding parts of an AI system, such as learning processes. Much of AI’s functionality emerges from data-driven learning, a significant shift in software design.
Artificial General Intelligence (AGI): AGI is not just an advancement in AI technology; it represents a potential shift in the paradigm of intelligence and problem-solving. The goal is to develop machines that can understand, learn, and apply intelligence broadly across various tasks, much like humans. Unlike Narrow AI, which is designed to perform specific tasks, AGI can theoretically apply its intelligence to solve any problem, including those for which it has not been specifically programmed. This level of AI will be capable of generalizing its learning and reasoning from one domain to another, demonstrating adaptable, flexible, and contextually aware intelligence. The significance of AGI lies in its potential to surpass human cognitive abilities. It embodies the concept of a machine that mimics human intelligence and can excel beyond human limits in various cognitive tasks. It hints at a future where AI will become more intelligent than humans, fundamentally changing our relationship with technology.
Automation: The application of technology for task execution without human intervention, often involving AI for decision-making and efficiency.
Autonomous Systems in AI: These systems independently formulate and execute plans to achieve set goals, which is crucial for robotic navigation without constant human oversight.
Chatbots: AI-driven programs simulating human conversation, widely used in customer service for automated, interactive responses.
Data Mining in AI: The process of sifting through large data sets to uncover patterns crucial for developing and refining machine learning models.
Deep Learning: An advanced form of machine learning, deep learning employs extensive neural networks to improve data interpretation, excelling in tasks like image and speech recognition.
Generative AI: AI capable of creating new, realistic content from trained data, including text, images, and music. This would include widely used technology such as ChatGPT, Midjourney, and Pika.
GPT: Generative Pre-trained Transformer (GPT) series by OpenAI represents a significant advancement in natural language processing and artificial intelligence. “GPT” stands for Generative Pre-trained Transformer, indicating its foundational architecture and purpose. Each iteration of GPT has built upon its predecessor, offering increasingly sophisticated language understanding and generation capabilities.
Intelligence in AI Context: In AI, intelligence is the capacity to learn, solve problems, and achieve objectives in a constantly changing environment, going beyond the capabilities of pre-programmed, non-intelligent machines.
Machine Learning (ML): A branch of AI, ML is about teaching computers to enhance their understanding and decision-making based on data analysis. It’s an interdisciplinary field, drawing from areas like statistics and neuroscience.
Narrow AI: AI systems specialized in single-task operations, like recognizing speech or faces.
Natural Language Processing (NLP): AI field enabling machines to interpret, understand, and respond to human language, as seen in technologies like ChatGPT.
Neural Networks: These are AI’s brain-inspired systems designed to identify patterns and relationships in data, playing a crucial role in complex data processing tasks.
Reinforcement Learning: Here, an AI agent learns to perform sequences of actions that maximize rewards, fostering autonomy without direct instruction on optimal strategies.
Robotics: The creation and use of AI-integrated robots for automating agricultural processes in sectors like cannabis.
Supervised Learning: A machine learning technique where computers learn from labeled data, like categorizing images of dogs based on provided labels.
Turing Test: A benchmark proposed by Alan Turing to assess if a machine’s intelligence is distinguishable from human intelligence.
Unsupervised Learning: This approach doesn’t rely on labeled data. Instead, it involves the system setting its tasks, such as predicting the next word in a sentence.
