Lesson 5:
Thinking machines
Year 8 – Computing systems
Starter activity
Words matter
Define ‘artificial’.
examples: flavour, flower, hair, heart, lake, light, materials
Provide synonyms for ‘intelligent’.
synonym – a different word with the same meaning
When would you call a person, an animal, or a machine ‘intelligent’?
Think, write, pair, share Use your worksheet to write down your answers and discuss them in pairs.
Starter activity
Created by humans, usually as a copy of or substitute for something natural
Words matter
Define ‘artificial’.
examples: flavour, flower, hair, heart, lake, light, materials
Starter activity
Define ‘artificial’
examples: flavour, flower, hair, heart, lake, light, materials
Provide synonyms for ‘intelligent’
Created by humans, usually as a copy of or substitute for something natural
astute, clever, creative, imaginative, ingenious, insightful, inventive, knowledgeable, perceptive, rational, smart, thinking
Words matter
Starter activity
Define ‘artificial’
examples: flavour, flower, hair, heart, lake, light, materials
Provide synonyms for ‘intelligent’
Created by humans, usually as a copy of or substitute for something natural
astute, clever, creative, imaginative, ingenious, insightful, inventive, knowledgeable, perceptive, rational, smart, thinking
When would you call a person, an animal, or a machine ‘intelligent’?
Words matter
Objectives
Lesson 5: Thinking machines
In this lesson, you will:
Activity 1
Defining artificial intelligence
Question .
What is artificial intelligence?
Answer . (suggestion)
Any machine that performs tasks that typically require intelligence in humans
There is no single, agreed
definition for artificial intelligence.
Activity 1
Artificial intelligence: what it is not (yet)
We are years away from achieving the kind of general AI portrayed in books and films.
At present, artificial intelligence research mostly focuses on individual aspects of intelligent behaviour.
The legendary HAL 9000 computer, from the film
2001: A Space Odyssey
Activity 2
Can a machine do this?
Task | Progress so far |
Play board games checkers, chess, Go | |
Prove mathematical propositions | |
Planning and scheduling | |
Checkers was solved in 2007: computers play perfectly.
Deep Blue by IBM beat the top human player in chess in 1996.
Humans haven’t beaten a top chess program since 2005.
AlphaGo by DeepMind beat the top human player in Go in 2017.
Automated provers have deduced thousands of known or new propositions and also discovered shorter proofs.
Computers are used extensively in manufacturing, crew scheduling, self-driving vehicles, and space exploration.
Questions . Do these tasks require ‘thinking’ by humans?
Do you think computers can perform these tasks well?
Activity 2
AI has by now succeeded in doing essentially everything that requires ‘thinking’ but has failed to do most of what people and animals do ‘without thinking’ – that, somehow, is much harder!
❠
Donald Knuth, author of The Art of Computer Programming, in 1981
Can a machine do this?
Activity 2
Task | Progress so far |
Identify objects in images | |
Identify words in sound | |
Generate speech from text | |
Handle and manipulate objects | |
Walk | |
Accuracy has jumped from 50% to 90% since 2011
Major advances since 2009
Error rates have dropped to around 5%
Comparable to professional transcribers
Major advances in 2016
Now almost indistinguishable from a real human voice
Robotic arms that pick up objects constantly improving
Mostly in research phase as of 2020
Two- and four-legged robots constantly improving
Mostly in research phase as of 2020
Can a machine do this?
Activity 2
The AI effect
Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation.’
❠
Rodney Brooks, director of MIT Artificial Intelligence Lab, from a 2002 Wired magazine article
Once something becomes useful enough and common enough it’s not labelled AI anymore.
❠
Nick Bostrom, director of the Future of Humanity Institute at Oxford University,
from a 2006 article at cnn.com
AI is whatever hasn’t been done yet.
❠
Douglas Hofstadter, in his 1979 book Gödel, Escher, Bach
Activity 2
The AI effect
Activity 2
The AI effect
Activity 2
Task | Progress so far |
Hold a conversation | |
Translate between languages | |
Understand and answer questions | |
Drive a car | |
Diagnosing medical images | |
Holding an open-ended conversation with a human is considered a benchmark for AI (the Turing test)
No chatbot has really achieved that goal as of 2020
Major advance by Google in 2016
Systems produce useful output, but still an open problem
Watson by IBM beat the top human players on Jeopardy! in 2011
It is capable of providing evidence to justify its answers
Highly complex problem�Major breakthroughs since 2005
Cases of performance comparable to human experts reported since 2012
Can a machine do this?
Activity 2
Most of the recent advances in artificial intelligence are due to breakthroughs in machine learning.
Can a machine do this?
Activity 3
For some tasks, providing explicit instructions is far too complicated.
Method: Program the machine to perform the task.
Provide the machine with explicit instructions.
The story so far
Goal: Create a machine that performs a specific task.
Identify objects in images |
Identify words in sound |
Generate speech from text |
Handle and manipulate objects |
Walk |
Hold a conversation |
Translate between languages |
Understand and answer questions |
Activity 3
Machine learning
Goal: Create a machine that performs a specific task.
Method: Program the machine to perform the task.
Provide the machine with explicit instructions.
Method: Teach the machine to perform the task.
How can this be achieved?
Tip: Think about how humans are taught.
Goal: Create a machine that performs a specific task.
Activity 3
Machine learning
Method: Teach the machine to perform the task.
Provide the machine with examples
Goal: Create a machine that performs a specific task.
Training examples to teach a machine how to recognise ducks
Program the machine to learn from examples
This is called ‘supervised learning’.
Activity 3
Machine learning
Method: Teach the machine to perform the task.
Provide the machine with feedback
Goal: Create a machine that performs a specific task.
Program the machine to learn from feedback
This is called ‘reinforcement learning’.
Training examples to teach a machine how to play noughts and crosses: a win results in positive feedback, a loss in negative
Activity 3
Machine learning
Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.
❠
Some Studies In Machine Learning Using the Game of Checkers
Arthur Samuel (1959)
Machine learning does not eliminate programming.
It replaces the problem of programming a machine to perform a task with two separate problems:
programming the machine to learn, and providing it with the necessary training
Activity 3
Machine learning
Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s?
If this were then subjected to an appropriate course of education one would obtain the adult brain.
We have thus divided our problem into two parts: the child-programme and the education process.
❠
Computing Machinery and Intelligence
Alan Turing (1950)
Machine learning does not eliminate programming.
It replaces the problem of programming a machine to perform a task with two separate problems:
programming the machine to learn, and providing it with the necessary training
Activity 4
Be the teacher
Teach your computer to tell the difference between apples and oranges, using Google Teachable Machine.
Follow the instructions in your worksheet.
Plenary
Thinking beyond ‘coolness’
Applications of AI | Moral considerations |
Self-driving cars | |
Medical diagnosis | |
Banking Detecting fraud Approving loan & mortgage applications | |
Automation Performing tasks instead of humans | |
Who is responsible in an accident? (Accountability)
How can decisions be explained? (Transparency)
How can we guarantee that machine training does not lead to discrimination? (Bias)
How can decisions be explained? (Transparency)
How will humans handle lower demand for labour?
How will the benefits of AI be fairly distributed?
Summary
In this lesson, you...
Next lesson, you will...
Take a quiz, to assess learning
Explore the implications of sharing programs, and learn about free and open source software
Defined artificial intelligence and machine learning
Explored examples of where they are being applied
Taught a machine how to recognise different types of images
Discussed moral issues associated with these technologies