3.7
Artificial Intelligence
Artificial intelligence is built around several key concepts, including:
knowledge representation
natural language processing
neural networks
machine learning
planning.
Big Data
The field of big data intersects with AI because machines need a lot of data to train their algorithms.
Data is classically quantified by the four Vs:
volume – the amount of data
velocity – the speed at which the data is generated
variety – the different types of data
veracity – the trustworthiness of the data in terms of accuracy.
he EU’s General Data Protection Regulation (GDPR) was an important step in defending the individual’s rights over their data and stopping abuse by corporations.
Data analytics
Data analytics is used to observe trends and make predictions.
MATLAB is a key tool for data science and statistical analysis.
The Apache SparkTM suite of libraries for processing and performing ETL (extract, transform, load) operations on Hadoop data includes:
Structured Query Language (SQL) – the industry standard for searching relational databases
Spark Streaming – high-throughput processing of live data streams
MLib – machine learning and statistical analysis
GraphX – graphing
AI tools for businesses IBM Watson, Microsoft Azure, Amazon Web Services, Google Data Studio, Oracle, and SAP Analytics Cloud.
Fundamentals of AI
AI and knowledge
AI is a branch of computer science which aims to create intelligent machines
This involves developing computer programs to complete tasks that would otherwise require human intelligence
Knowledge is a core part of AI
AI aims to provide machines with knowledge capacity and algorithms to perform functions such as learning, perception, problem-solving, understanding language and logical reasoning without human intervention.
AI
Machine Learning
Deep Learning
natural language processing (NLP)
knowledge representation and reasoning
optimisation methods and decision making
Knowledge representation is concerned with representing information in a form that a computer algorithm can easily navigate and interpret
Alan Turing, the pioneer of AI
Turing Test
The final question for AI is: can a computer be conscious of its own existence? This, and other more philosophical questions about AI will be covered in Unit 6.
Case Studies
AI in advertising
AI in medicine
use in diagnostic medicine
It can identify patterns and detect irregularities better than a human
Using AI to diagnose disease leaves clinicians with more time to spend treating patients.
Precision medicine is the customisation of healthcare, with medical diagnosis and treatment that is tailored to the individual patient.
Big data and healthcare
To make sense of big data, data analytics is required, including:
predictive analytics – forecasting behaviours and future developments
machine learning – using algorithms to analyse large data sets.
data mining – data sets are searched for patterns and relationships
Advanced patients care
Improved operational efficiency
Improved Treatment
Electronic health records will help to obtain demographic and medical data such as lab tests, clinical data, diagnosis and medical conditions.
Big data can be used as part of a business intelligence strategy to analyse historical patient admission rates and staff efficiency.
Big data can help to identify unknown correlations, hidden patterns and insights by examining significant amounts of data.