The ABCs of AI: 20 key terms every police officer should know
From facial recognition to machine learning, here’s a glossary of AI terms that can help police officers leverage the power of artificial intelligence to prevent crime and improve public safety
By Police1 Staff
The use of artificial intelligence (AI) tools is rapidly transforming the field of law enforcement, allowing police officers to work more efficiently and effectively. From predictive policing to facial recognition, AI technologies are being used to prevent crime, solve cases and improve public safety. We prompted ChatGPT to compile a list of 20 important terms related to AI and law enforcement that every police officer should know.
This glossary of AI terms was generated by ChatGPT, a large language model trained by OpenAI, based on the GPT-3.5 architecture. ChatGPT uses advanced machine learning algorithms to understand and respond to natural language inputs, making it a powerful tool for a range of applications, including law enforcement. With ChatGPT, police officers can quickly and easily access information about AI technologies and their applications in law enforcement, helping them to stay up-to-date on the latest developments in the field.
Some of the key terms covered in this glossary include machine learning, natural language processing, facial recognition, and big data. By understanding these concepts, police officers can better leverage the power of AI tools to prevent crime, improve investigations, and enhance community safety. With this glossary of AI terms, we aim to provide a comprehensive introduction to the world of AI in law enforcement, helping police officers to develop the knowledge and skills they need to succeed in the field.
Artificial Intelligence (AI)
The development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning (ML)
A subfield of AI that enables machines to learn from data without being explicitly programmed, allowing them to improve their performance over time.
Natural Language Processing (NLP)
A subfield of AI that deals with the interaction between computers and humans using natural language, allowing machines to understand, interpret, and generate human language.
Computer Vision (CV)
A subfield of AI that focuses on enabling machines to interpret and analyze visual data from the world, such as images and videos.
A subfield of ML that involves training artificial neural networks with multiple layers to learn complex patterns in data, allowing them to perform tasks such as image recognition and natural language processing.
The use of data analysis and AI tools to identify and prevent crime before it occurs, by analyzing patterns and trends in crime data to predict where and when crimes are most likely to occur.
The use of computer algorithms to identify or verify the identity of a person based on their facial features, by comparing their facial characteristics to a database of known faces.
License Plate Recognition (LPR)
The use of optical character recognition (OCR) technology to read and capture license plate numbers from images or video footage, enabling automated identification of vehicles.
Crime Pattern Analysis
The use of data analysis and visualization tools to identify patterns and trends in crime data, enabling law enforcement to make more informed decisions and allocate resources more effectively.
The use of NLP techniques to extract subjective information from text, such as opinions, emotions, and attitudes, enabling law enforcement to monitor public sentiment and identify potential threats.
Robotic Process Automation (RPA)
The use of software robots to automate repetitive tasks and processes, such as data entry and record keeping, freeing up human resources for more complex tasks.
A type of AI that uses natural language processing and machine learning to mimic human thought processes, enabling machines to understand and analyze unstructured data, such as text and images.
The use of unique physical or behavioral characteristics, such as fingerprints, iris scans, and voice recognition, to identify individuals and verify their identity.
The process of analyzing large datasets to extract valuable insights and patterns, using statistical and machine learning techniques to identify correlations and trends.
The protection of computer systems and networks from theft, damage, or unauthorized access, using a range of techniques and tools, such as encryption and firewalls.
Automated Decision Making
The use of AI and machine learning algorithms to make decisions without human intervention, based on predefined rules and parameters.
Internet of Things (IoT)
A network of physical devices, vehicles, and other objects embedded with sensors, software, and connectivity, enabling them to exchange data and interact with each other.
Extremely large datasets that cannot be processed or analyzed using traditional methods, often requiring specialized tools and techniques, such as distributed computing and machine learning.
The tendency for AI algorithms to exhibit bias or discrimination, based on factors such as race, gender, and socioeconomic status, due to the data used to train them.
Explainable AI (XAI)
AI systems that are designed to be transparent and understandable, enabling humans to understand how they make decisions
Complete the box below to download this list for easy reference and test your AI knowledge here.