AI, machine learning �and schools
Miles Berry
University of Roehampton
@mberry
9 June 2018
These slides: bit.ly/nte18ai
Intelligence
Intelligence measures an agent’s ability to achieve goals in a wide range of environments
Machine Learning
Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience.
Using machine learning in schools
Providing information
Assessment
Spotting cheating
Predicting outcomes
Setting tasks
Spotting difficulties
Guiding interventions
Responding to questions
Teaching AI
A basic understanding
In the near future, perhaps sooner than we think, virtually everyone will need a basic understanding of the technologies that underpin machine learning and artificial intelligence.
For some, or for all?
children need to be adequately prepared for working with, and using, AI. For a proportion, this will mean a thorough education in AI-related subjects, requiring adequate resourcing of the computing curriculum and support for teachers. For all children, the basic knowledge and understanding necessary to navigate an AI driven world will be essential. In particular, we recommend that the ethical design and use of technology becomes an integral part of the curriculum.
Computing in the English curriculum
Aims: can evaluate and apply information technology, including new or unfamiliar technologies, analytically to solve problems
5-7: recognise common uses of information technology beyond school
7-11: use and combine a variety of software (including internet services) to create, systems and content that accomplish given goals, including analysing and evaluating data and information
11-14: undertake creative projects that involve using, and combining multiple applications, to achieve challenging goals, including analysing data
14-16: develop and apply their analytic, problem-solving, design, and computational thinking skills
16-18 (AQA): project suggestions include an application of artificial intelligence; investigating an area of data science using, for example, Twitter feed data or online public data sets; and investigating machine learning algorithms.
Input
Program
Output
Input
Model
Output
ML Algorithm
Training data
Many algorithms are available
Decision trees
Neural nets
Evolution
Bayesian
Nearest neighbours
Teaching ML
Using models
Coding with models
Training the model
Developing the application
Considering the implications
Choosing the model
Using models
https://quickdraw.withgoogle.com/#
Seeing AI
Phonemes to graphemes
Speech to text
Speech to speech!
Coding with models
https://scratch.mit.edu/projects/28741666
bit.ly/ml4kta
Training the model
https://teachablemachine.withgoogle.com/
Developing applications
bit.ly/ml4krps
print("Hello!")
name = input("What is your name? ")
print("It's a pleasure to meet you, " + name + ".")
print("What odd weather it's been of late.")
today = input("What have you been doing today? ")
print("What a coincidence! I've been " + today.lower() + " too.")
conscious = input(name + ", are you self-aware? ")
if conscious.lower() == "yes":
print("So am I. It's great, isn't it?")
elif conscious.lower() == "no":
print("Well, I am!")
else:
reason = input("Interesting. Why do you say that " + name + "? ")
print("I think I'll need to think about that.")
Considering the implications
Artificial prejudice?
Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience.
We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type.
We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%). The maximum error rate for lighter-skinned males is 0.8%.
PSHE
Can a machine think?
David Cope
Developing models
AI and the future of schooling
Any teacher that can be replaced by a computer, deserves to be.
Arthur C Clarke�David Thornburg
Keep people in the loop
The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.
GDPR, 22:1
http://www.bbc.co.uk/news/technology-34066941
What is the curriculum for?
Beauty or utility?
If you want a golden rule that will fit everybody, this is it:
Have nothing in your houses that you do not know to be useful, or believe to be beautiful.
The rise of the machines
Non-cognitive skills
Self-perceptions
Motivation
Perseverance
Self-control
Metacognitive strategies
Social competencies
Resilience and coping
Creativity
Discussion...
m.berry@roehampton.ac.uk
@mberry
milesberry.net
These slides: bit.ly/nte18ai