FRSC 3100 Trends Flashcards
AFIS
Automated Fingerprint Identification Systems
Uses rolled fingerprints, plain or flat, lower palm, upper palm and writers palm
Contains 4 mil records
How does AFIS interpret data
biometric computer system
locates and measures reliable and persistant features within the fingerprint and palm print images i.e. minutiae
Narrow AI
Ai designed to do 1 specific task (or narrowly related set of tasks). Virtually all AI today.
Areas of use: AI chatbots, cancer detection, protein analysis, recommendations for shopping/viewing, text-to-speech/speech to text, google maps
Generative AI
Ai that creates some kind of ‘new content’.
Areas of use: stable diffusion = Art AI, Chat GPT
Non-Generative AI
Speech recognition, Recommended systems, navigation mapping and applications
Large Language Models (LLM)
AI program designed to understand, generate, and work with human language on a large scale. Generate coherent and contextually relevant text based on the input they receive. Fed large datasets containing a wide array of text, from which they learn language, patterns, structure, and nuances
Areas of use: translation, summarization, answering questions, creative writing
Natural Language Processing (NLP)
focuses on the interaction bw computers and human language. Involves enabling computers to understand, interpret, & respond to human language in a way that is both meaningful/useful.
Areas of use: chatbots and virtual assistants (siri, Alext, etc), “hey google” voice interfaces, customer automation, content categorization in media, email filtering, language translation services.
Application programming Interface (API)
protocols that allow different software programs to communicate with each other/AI.
Areas of use:
Text analysis: sentiment analysis, language detection, text summarization, etc.
Image recognition: object detection, factual recognition, etc.
Natural language processing: language translation, text-to-speech, speech-to-text, etc.
Voice interface: language translation, text-to-speech, speech-to-text, etc
Machine Learning
use data and algorithms to mimic human learning. Statistical methods to train algorithms to classify or predict and even provide insights into data mining projects.
Text Generation (error-prone, lack of credit)
Image compilation (Copy-right issues, Deepfakes)
Pattern Recognition (AFIS, Facial Recog)
Areas of use of ML
Areas of use:
More diverse in application & can be more efficient for simpler tasks/when working w smaller dataset
Deep Learning
subset of ML. Uses neural networks to analyze and learn from data. Well suited for processing unstructured data like images and text. ‘Complex’ = have multiple layers that automatically detect and learn hierarchical feature representations.
Areas of use: powerful where learning from vast amounts of data directly is beneficial. Image and speech recognition.
Appearance Based Facial Recognition
Scraping: what some AI’s illegally do. Give it an image and it searches the entire internet for anything related.
What are some future trends in forensic science
increased use of DNA analysis and genetic profiling
Development of new technologies for trace evidence analysis and interpretation
Integration of artificial intelligence and machine learning into forensic analysis processes
Increased use of virtual and augmented reality for crime scene reconstruction and simulation
Increased emphasis on forensic psychology and behavioral analysis
Greater emphasis on forensic anthropology and human identification
Development of portable and handheld forensic analysis tools for use in the field
Increased use of biometric identification methods, such as facial recognition and fingerprint analysis
Greater collaboration and sharing of forensic data and information among law enforcement agencies and laboratories.
Historical Context: Pre 1900
Caesar/ forum
1302 first legal autopsies
1590 microscope invented
1832 James marsh develops first arsenic test
1835 first bullet comparison
Advances in the 1800s
Using evidence documents known as questioned documents
The invention of the polarized light microscope, often used today in fiber analysis
Using photography for criminal identification and crime scene documentation
Identifying insect stage development in corpses to determine the time elapsed since death
1888 London Jack the Ripper: first major investigation to follow the processes/practices still in use (post-mortem, scene documentation, interviews, statements, etc) today
1892 fingerprints - the first crime solved by fingerprint analysis and the calculation by Francis Galton that fingerprints had only a 1 in 64 billion chance of being alike.
Advances in the 1900s
1900, different human blood types, ABO, are discovered by Karl Landsteiner. allowing crime scene investigators to match blood from a victim to blood at the scene.
1902, the first academic curriculum for forensic science was developed in Switzerland
1905, President Theodore Roosevelt established the FBI, the Federal Bureau of Investigation
Alphonse Bertillon developed an anthropometric system of identifying criminals
Chromatography - Russian botanist Mikhail Tsvet invented column chromatography in 1906
In 1910 first forensic police crime lab was created in Lyon France by Dr. Edmund Locard
Moore’s Law
number of transistors on a microprocessor chip and the cost of transistors should increase in two years
Douglas Adams
Came up with a set of rules that describes our reactions to technologies:
Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works
Anything that is invented anytime between the time you’re 15 and 35 is new and exciting and revolutionary and you can probably get a career in it
Anything invented after you’re 35 is against the natural order of things
What is a disruptive technology
is an innovation that significantly alters how consumers, industries, or businesses operate.
Disruptive technology supersedes an older established process, product, or habit with recognizably superior attributes.
Examples of recent disruptive technologies
Recent disruptive technology examples include e-commerce, online news sites, ride-sharing apps, and GPS systems.
Who introduced the idea of disruptive technologies
Clayton Christensen
AI is coupled with 3 things
- Extremely high computing speed
- large volume dataset processing
- the ability to self-learn
Generative AI
focuses on understanding patterns and structure in data and using that to create new data that looks like it.
This includes writing blocks of text, lines of code or creating photorealistic images Predictive Discriminative AI focuses mainly on classification, learning the difference between “things” - cats and dogs, for example.
Levels of artificial intelligence
ANI - ARTIFICIAL NARROW INTELLIGENCE
AGI - ARTIFICIAL GENERAL INTELLIGENCE
ASI - ARTIFICIAL SUPER INTELLIGENCE