5 Automate, emerging tech

• Automated systems


  1. Sensor - measure environment, send data to ADC, send binary/digital data to process

  2. Microprocessor - process data, compare to stored data, determine action, send signal to actuator

  3. Actuator - create a movement

  4. Process repeats until turn off
• Automated systemspros

  • Higher productivity, efficiency, yield
  • Changes in environment can be identified quickly
  • Safer - no human error
  • Precision and accuracy of tasks
  • Less expensive in long run - profit from yield, no employees
  • Scalable and flexible to accommodate changes in demand and adjust production levels
• Automated systems

  • Expensive to setup and maintain
  • Computerised systems prone to attacks
  • Job displacement and unemployment
  • Reduced human interaction and customer experience
  • Technical challenges - no internet, malfunctions, cybersecurity vulnerability -> cause disrupt operations or harm
• Robotics

  design,

  construction,

  operation of robots

  -> perform tasks/operations/functions
• Robots

  • Mechanical structure/framework
  • Electrical components e.g. sensors, microprocessors and actuators
  • Programmable
• Robots pros

  • Work 24/7
  • SAFE? Work in dangerous conditions
  • COST? Less expensive in long run
  • YIELD/ACC? High productivity, more consistent
  • TO Workers: utilise skills in other tasks, don't need perform repetitive tasks,protect from danger/no lift heavy
• Robots

  • Difficult to do non-standard tasks
  • UNEMPLOY: Loss of skills as replace humans
  • COST? Expensive to install (company need money to pay up front, employees need TRAINING)
  • High maintenance cost (utility, electricity bill, skilled workers, need UPDATING)
  • HACKED? Risk compromise
  • MALFUNCTION? - production stop
• Artificial Intelligence (AI)


  A branch of computer science dealing with the simulation of intelligent behaviours by computers
• AI


  • Collection of data: input
  • Programmed rules for using that data: stored in program
  • -> decisions
  • logically reason: & develop facts from set rules
  • learn: adapt own logic/rules (training)
• Expert systems


  1. Knowledge base: list of facts

  2. Rule base: links the facts in knowledge base, stores rules for system for interference engine to use

  3. Inference engine: applies rule base to knowledge base, decides which question/answer to give, apply logic to facts to provide result

  4. Interface: view data output and allow user to input data
• Expert systems


  Emulate the expertise of a human, ask user questions to determine solution or answer, apply rules/logic to knowledge to provide result/diagnosis
• Machine learning


  1. Program can automatically adapt its own processes and/or data

  2. Edit its own data

  3. Analyse pattern

  4. Store successful/unsuccessful results - to influence future decisions
• Machine learning training


  • Trained - learn w/o human interaction
  • Supervised: user tells system what data means/input and output
  • Unsupervised: system given input, need to work out output
• AI features

  • Collects data
  • Stores rules for using data
  • Ability to reason / learn / use machine learning
  • Makes multiple predictions to make decision
  • Find/analyses data
  • Process for maze puzzles
• AI process for maze puzzles

  1. Use machine learning algorithm

  2. Collects data about where it has been, obstacles/problems

  3. Stores successful + unsuccessful actions

  4. Identify/store patterns to not repeat same incorrect route, know how to react to obstacles next time, know what is most likely to work next time