Presentation delivered at BETT AHEAD 2023

1. How to Teach and
Learn with ChatGPT:
AI and new horizons for
assistive technologies
Dominik Lukes, EduTools.fyi

2. Lots of
developments since
we scheduled the
session

3. What has changed
Bing
Chat
GPT-4
Google
Bard
Office
‘Co-
Pilot’s
GPT-4
Plugins

4. bit.ly/academic-ai-newsletter
https://bit.ly/academic-ai-newsletter

5. Poll:
What is your experience
with ChatGPT?

6. Poll: Experience?
Use it every day
Played around with it
Heard a LOT about it
I heard something about it

7. Comparison from a recent talk

8. Poll:
What do you think AI
might be best at?

9. What do you think AI might be best at?
Looking up facts
Performing complex calculations
Writing computer code based on text
Making complex decisions from data
Writing poetry
Making beautiful art

10. Comparison with a recent talk

11. What it is actually
good at?

12. Nobody expected AI to be any good at this

13. Or this

14. We thought AI could do this

15. But it is also unexpectedly good at this

16. How do we know what
modern AI will be good or
bad at?

17. We have evaluate it
systematically. Almost as a
new organism.

18. Hofstadter, 1979: Questions
Question:
Will a thinking computer be able
to add fast?

19. Hofstadter, 1979, Speculations
Speculation:
Perhaps not. We ourselves are
composed of hardware which
does fancy calculations but that
doesn't mean that our symbol
level, where "we" are, knows
how to carry out the same fancy
calculations.

20. Let’s keep this in mind

21. What ChatGPT can
do for YOU?

22. Better at poetry than math
still means it can be
incredibly useful.

23. Anyone can use it to help with reading

24. A student
can use this
for help

25. A teacher
can use it
to create
tools

26. There are limits but also,
we can use it to assist us
in real ways. But we
should understand what it
is doing.

27. 5 Things about
ChatGPT in 15(ish)
Minutes
Dominik.Lukes@ctl.ox.ac.uk

28. 5 things
about
ChatGPT in
15 minutes
It has many building blocks
It does not reason, it predicts
It is limited by the context
window
It has no database, copy or
paste or memory
It does not learn but you can
correct it

29. Building blocks of
modern AI

30. What does ChatGPT stand for?
Chat
answers questions rather than
continues
G - generative
creates strings rather than
labels
P - pretrained
can be fine-tuned on much less
data
T - transformer
can be trained to predict from
long context
Foundation
model

31. Building blocks of the AI revolution
Foundation Models
Products
ChatGPT Consensus …
Features
Notion … PowerPoint… …

33. Building blocks of a product
Foundation Model API
Fine tuning on
custom data
Consensus
(OpenAI)
Notion
(Anthropic)
Smart Reply
(Google)
Finetuning by
human feedback
ChatGPT
(Open AI)
Claude
(Anthropic)
Bard (Google)
Custom
Workflow /
Interface
TeacherMatic LangChain…

34. For example Consensus (now with GPT-4)

35. ChatGPT is just another product on top of GPT

36. All of these work the same way

37. Big API providers
OpenAI
Anthropic
Google Amazon
Microsoft
Meta

38. Although there are many smaller ones on HuggingFace

39. Thousands API customers

40. And thousands more

41. Always ask what API a
product uses!

42. It does not reason, it
predicts

43. It is easy to be deceived
by appearances.

44. ChatGPT can do this

45. How do we solve these
kinds of problems?

46. Cognition = Computation
Logic
Compare
Organise
Transform
Memory
Store
Retrieve

47. Something like this

48. How does ChatGPT solve
these kinds of problems?

49. ChatGPT does not have cognition or computation
Logic
Compare
Organise
Transform
Memory
Store
Retrieve

50. ChatGPT Process
Break all
preceding text
into tokens
Assign each
token number
Predict next
token
Repeat with
the new token

51. What and why of
tokens? And why it
matters. A lot!

52. How we see text?
Humans
Letters
Words
Sentences
Paragraphs
Texts
ChatGPT
Tokens

53. OpenAI: ChatGPT only sees tokens

54. Actually, this is what ChatGPT sees
Unicode

55. Numbers are not digits, just tokens

56. Fun facts about OpenAI tokens
About 50,000 in total
Only 3,000 English words
Most parts of words incl. letters, spaces
About 140 tokens per 100 English words
Much more for other languages

57. Word and tokens are only accidentally identical

58. Unexpected consequence: More tokens more money
English
French
Czech
Chinese
Arabic

59. Consequences of tokens
Limited rhyming
Bad at letter tasks
Unreliable numbers
More grammar mistakes in
some languages

60. It’s not good at
this BUT it
shouldn’t be
able to do this
at all.

61. BTW: GPT-4 is
not much better
at this.

62. Tokens are not
graphics, but
they can still
produce this.

63. Embeddings: Give
tokens meaning

64. What are embeddings?
Embeddings show how tokens
relate to each other in
multidimensional-dimensional
space.

65. Language embeddings

66. You do maths on matrices and you get
king man woman queen

67. But all of this
is happening
in 100s or
1000s or
dimensions.

68. More complicated than simple matrix addition

69. Add it all up

70. All it is doing on the inside
is multiplying matrices.

71. Really big ones, for every
single token in the context
window!

72. On hundreds or
thousands of
expensive
graphics cards.

73. This means it costs $$$
every time you ask it to do
something.

74. Generate predictions

75. The we predict what comes next (from Wolfram)

76. The top item is not always selected

77. Turn up the
temperature:
Randomness

78. With heat

79. What it looks like with temperature 1

80. Without heat

81. What it looks like with temperature 0

82. Remember all of this happens for each new token

83. Context window is a
limit on prediction

84. GPT-3.5 has a context
window of 2,000 tokens.
GPT-4 has 8,000 –
32,000.

85. Every new token
generated, eats into the
context window!

86. 2,000 word summary by GPT-3.5 with 2k token window

87. Same summary by GPT-4 with 8k token window

88. There is no database

89. It looks like it’s looking
things up, copying them
and pasting them. BUT

90. It always just predicts
https://what-if.xkcd.com/34

91. But because it was
trained on almost
trillion tokens and has
175 billion parameters,
it can do this.

92. It can even
do this!

93. But it because it always
just predicts:
No fact can be trusted.
No common sense can
be expected!

94. It really cannot copy and
compare any information!!!

95. Is there a number? DO NOT TRUST?

96. Can we trust the results of a search?

97. It can count some things but not very far

98. Bing will give references but the information is not there

99. It does not even have
access to its own training
data!

100. Bret Devereaux
[ChatGPT] has read every
book in the library once and
distilled the statistical
relationships between the words
in that library and then burned
the library.

101. ChatGPT does not even try

102. ChatGPT sometimes
hallucinates!

103. ChatGPT always
hallucinates!

104. It just happens to match
what we need often
enough to be really useful.

105. How much does
predicting the next
token get us?

106. Surely, it could not count
how many things are
mentioned in text just by
predicting the next token!

107. How good is it at counting things

108. How good is it at counting intentions

109. Can it take a hint?

110. How good is it at sorting?

111. Can it do metalanguage?

112. GPT-4 Improves things

113. But only up to a point

114. But it is close

115. Every time you try a number try it more than once

116. Agrawal
Computer graphics = Cheap
arithmetic
AI = Cheap prediction

117. In 1950s we could not
imagine all the things
cheap arithmetic
would gets. Today, we
can’t appreciate what
cheap predictions will
bring.
Computer room in 1949

118. We did not have good
intuitions about what
cheap arithmetic will get
us. What will cheap
prediction?

119. Four years ago, Google made a big announcement

120. Major progress in 2019

121. And 4 years later

122. Also No idea of
spatial relations
No
understanding of
physics
No idea of social
relations

123. Space is a problem: Some attempts at a bicycle

124. Social relations are a problem
Customer:
Take me downtown Prague.
Taxi driver:
How much does the trip cost?

125. It does not LEARN
live but can be
corrected

126. What it took to train the foundation model
21 days $1+ million

127. What it took to fine tune it with human feedback
X months $$$ million

128. But it is a few-shot learner:
It can follow examples

129. And then… (GPT-3)

130. Take it through a problem step by step.

131. Time to do some
prompt engineering

132. The key lesson for
practical use

133. Keep 2 things in mind at once
Give it prompts
as to a human
being
Interpret the
results as if
autocomplete

134. What ChatGPT can do is a
matter of experimentation.

135. Prompt engineering
should aim towards giving
more examples and
context.

136. Key lessons from prompt engineers
Narrow down
context
1
Give
examples
2
Specify style
3
Specify
persona
4
Ask for
corrections
5

137. Don’t just ask, don’t just
tell.
Iterate!

138. Or in the words of ChatGPT

139. LearnPrompting

140. Three levels of editing
Shallow
prompt
Fine-
tuned
prompt
Co-
editing

141. Chain of Thought Prompts
Chain of Thought =
more accuracy
easier review

142. But it can do this

143. And this

144. Asking for structure is
better than prose!

145. Outlines
are very
context-
based

146. You can convert them into
interactive things

147. Asking for concepts and examples

148. Asking for timelines better than narratives

149. Asking to adopt a
persona is also
very useful,

150. You can even have a back and forth

151. Where to learn
more?

152. Edutools.FYI

153. bit.ly/academic-ai-newsletter
https://bit.ly/academic-ai-newsletter

154. Tools change,
people remain the
same
EduTools.fyi

155. This presentation is licensed
under Creative Commons By
Attribution license except where
otherwise noted.
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Content and cannot be reused
outside this document.