IB Computer Science Modeling and Simulation

Modelling

The process of creating a computer model of a system to allow for recording of relevant data and processing the data based on accurate rules to produce useful output.

Advantages of Spreadsheets for Mathematical Modelling

Mathematical models can easily be made using spreadsheet software which allows formulas to be created and copied for large amounts of data, allows values to be looked up from lookup tables and data to be recorded and updated without calculations having to be redone.

Simulation

A simulation is a process of varying the inputs and/or rules of a mathematical model to observe the effects.

Visualisations

Visualisations are graphical representations of the outputs of computer models or simulations. Whether 2D or 3D they can be beneficial in communicating complex data sets in a way that is easily and quickly understood by the audience e.g a large number of temperature and location measurements can be grouped and plotted on a map to be easily understood.

Limitations of Computer Models

Computer models are designed by humans and so are limited by the designer selecting the correct data. They are also limited by the computer resources such as memory secondary storage and processor time e.g. a model of global climate would require huge amounts of memory to store the many variables required.

Grouping for Collections of Data Items

Data in a model should be grouped to allow for clarity e.g. a model of atmospheric measurements taken around the world could be grouped by the date taken and organised on one row of a model. Grouping of data could also be used to find trends in data and see the correlations.

Test Case to Evaluate a Model

Test cases are used to identify if a model is functioning correctly. It specifies the inputs and expected outputs of a model. The actual outputs of the model are then compared with the expected outputs to identify any problems with the model.

Effectiveness of Test Case

- They need to test all possible inputs of a model, delete inputs and add inputs. (test for normal, extreme and erroneous data)

Discuss the Correctness of a Model

The correctness of a model is how well the model reproduces actual data observed in the real world system. The more accurately outputs of the model reflect the actual observed phenomena, the more correct it is.

Abstraction for Modelling

The process of modelling involves creating abstractions of other systems that focus on and include the relevant details of a system such as inputs, outputs and rules but do not include every detail about the system.

Difference between a Simulation and a Model

A model represents a real life system using mathematical formulas and algorithms while a simulation uses models to study the system by inserting different inputs and varying the rules and analysing the outputs.

Genetic Algorithms

A genetic algorithm (GA) is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions.

Visualisation

Producing graphs or animations based on computer data, to help understand and analyse it.

Memory Needs of 2D Visualisation

As simple as static graphics, memory requirements are similar to a graphic image. Interactive 2D visualisations involve several images and code which would occupy more space.

Ray Tracing

In computer graphics, ray tracing is a technique for generating an image by tracing the path of light through pixels in an image plane and simulating the effects of its encounters with virtual objects.

Lighting

Lighting plays an important role in how we perceive 3D objects. When rendering a 3D model the location, intensity and number of a light sources is modelled to calculate the brightness of each pixel in a rendered shape.

Key Frames

In animation and filmmaking, it is a drawing which defines the starting and ending points of any smooth transition.

Texture Mapping

Wrapping a texture map around a 3D object in order to make it appear more realistic

Structure of Neural networks

Input - the problem is inputed into the network. The value inputed is then multiplied with the weighting of the synapse.

Speech Recognition

The process of turning digitised audio of spoken words into text.

Optical Character Recognition (OCR)

Optical character recognition (also optical character reader, OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text. This usually involves the use of unsupervised learning to recognise the characters.

Natural Language Processing

Natural language processing is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages.

Supervised Learning

Type of machine learning in which the desired output is known. Therefore the output will be checked against the predetermined output and adjusted accordingly to analyse the next inputs better.

Unsupervised Learning

Type of machine learning in which their is no predetermined output. Seeks to identify unknown patterns within sets of data.

Difference between Human and Machine Learning

People learn through experience that subjectively relates words to past experience to give words their meanings. This is cognitive learning.

Cognitive Learning

the acquisition of mental information, whether by observing events, by watching others, or through language

Heuristics

Allowing someone to discover something by their selves.

Probabilities

Some natural language processing software uses probabilities of the combinations of certain words to try process and interpret natural language sentences.

Evolution of Modern Machine Translators

Early machine translators would just translate individual words in a dictionary. Next they would analyse grammar and dependencies and now they are able to translate whole phrases using probabilities and computer learning to develop models of commonly used phrases.

Role of Chatbots

Chatbots are algorithms that are available for people to "chat" with online. The fact that they are open for anyone to "chat" to means the algorithms can be tested large numbers of times by large numbers of different people and collect large amounts of data of actual human natural language. This data can then be analysed to identify common structures and phrases in use in natural language.

Latest Advances in Natural Language Processing

Some latest advances in natural language processing are algorithms that can interpret and action spoken commands such as Apples Siri on its smartphones and tablets. NLP is also being used to "mine" social media texts to gather data on health finances and to identify sentiment and emotion toward products and services (to be used for marketing purposes).

Difficulties in Machine language Translation

Although words can be recognised they could have many meanings.

Evaluate the advantages and disadvantages to society of the rapid and sophisticated analysis of information on social networks.

Important developments and views get published quickly; Such as in dangerous situations (war, earthquake etc);

Distinguish between the structures of natural language that can be learnt by robots and those that cannot.

A robot can be programmed to apply rules/syntax to natural language;

Discuss the differences between human and machine learning in relation to natural language processing.

Human cognitive learning/complex thought process