Following the history of computing, learning from Ada Lovelace's monumental paper.

2. Copyright of every illustration belongs to
Sydney Padua.
Hooray for her great book, The Thrilling
Adventures of Lovelace and Babbage.
Her book gave me the insight to organize all
these contents, so hooray on that too.

3. Copyright of every illustration belongs to
Sydney Padua.
Hooray for her great book, The Thrilling
Adventures of Lovelace and Babbage.
Her book gave me the insight to organize all
these contents, so hooray on that too.

4. Augusta Ada King, Countess of Lovelace
1815~1852

5. Augusta Ada King, Countess of Lovelace
1815~1852

6. Augusta Ada King, Countess of Lovelace
1815~1852

7. George Gordon Byron
1788~1824

8. “Byronmania”

9. The Bishop of Old Patras Germanos Blesses the Flag of Revolution
by Theodoros Vryzakis (1788~1824)

10. The Bishop of Old Patras Germanos Blesses the Flag of Revolution
by Theodoros Vryzakis (1788~1824)

11. Anne Isabella Noel Byron
1792~1860

12. Anne Isabella Noel Byron
1792~1860

13. Anne Isabella Noel Byron
1792~1860

14. Anne Isabella Noel Byron
1792~1860

15. “The princess of parallelograms.”

16. “The princess of parallelograms.”

17. “The princess of parallelograms.”

18. Augusta Ada King, Countess of Lovelace
(When she was 4)

19. Augusta Ada King, Countess of Lovelace
(When she was 4)

20. Augusta Ada King, Countess of Lovelace
(When she was 4)

21. Augusta Ada King, Countess of Lovelace
(When she was 4)

22. Augusta Ada King, Countess of Lovelace
(When she was 4)

23. Augusta Ada King, Countess of Lovelace
(When she was 4)

24. Mary Somerville
1780~1872

25. Mary Somerville
1780~1872

26. “The Queen of
Nineteenth-Century Science.”

27. Mary Somerville
1780~1872

28. Mary Somerville
1780~1872

29. Mary Somerville
1780~1872

30. Mary Somerville
1780~1872

31. Caroline Herschel
1750~1848

32. John Stuart Mill
1806~1873

33. John Stuart Mill
1806~1873

34. Mary Somerville
1780~1872

35. Augustus De Morgan
1806~1871

36. Lovelace’s sketch with De Morgan’s comments

37. “The potential to be an
original mathematical investigator,”

38. “The potential to be an
original mathematical investigator,”

39. George Boole
1815~1864

40. Charles Babbage
1791~1871

41. Charles Babbage
1791~1871

42. Charles Babbage
1791~1871

43. Charles Babbage
1791~1871

44. Charles Babbage
1791~1871

45. Michael Faraday
1791~1867

46. Charles Darwin
1809~1882

47. Alfred, Lord Tennyson
1809~1892

48. Alfred, Lord Tennyson
1809~1892

49. Charles Dickens
1812~1870

50. Florence Nightingale
1820~1910

51. Florence Nightingale
1820~1910

52. Difference Engine

53. Difference Engine

54. Newton's method of divided differences

55. Newton's method of divided differences

56. Newton's method of divided differences

57. “Degree decreasing when using divided differences”
𝒏 (𝒙 + 𝟏) 𝒏−𝒙 𝒏
1 𝑥 + 1 − 𝑥 = 1
2 (𝑥 + 1)2
−𝑥2
= 2𝑥 + 1
3 (𝑥 + 1)3−𝑥3 = 3𝑥2 + 3𝑥 + 1
︙ ︙

58. “Degree decreasing when using divided differences”
𝒏 (𝒙 + 𝟏) 𝒏−𝒙 𝒏
1 𝑥 + 1 − 𝑥 = 1
2 (𝑥 + 1)2
−𝑥2
= 2𝑥 + 1
3 (𝑥 + 1)3−𝑥3 = 3𝑥2 + 3𝑥 + 1
︙ ︙

59. “Degree decreasing when using divided differences”
𝒏 (𝒙 + 𝟏) 𝒏−𝒙 𝒏
1 𝑥 + 1 − 𝑥 = 1
2 (𝑥 + 1)2
−𝑥2
= 2𝑥 + 1
3 (𝑥 + 1)3−𝑥3 = 3𝑥2 + 3𝑥 + 1
︙ ︙

60. “Degree decreasing when using divided differences”
𝒏 (𝒙 + 𝟏) 𝒏−𝒙 𝒏
1 𝑥 + 1 − 𝑥 = 1
2 (𝑥 + 1)2
−𝑥2
= 2𝑥 + 1
3 (𝑥 + 1)3−𝑥3 = 3𝑥2 + 3𝑥 + 1
︙ ︙

61. “Degree decreasing when using divided differences”
𝒏 (𝒙 + 𝟏) 𝒏−𝒙 𝒏
1 𝑥 + 1 − 𝑥 = 1
2 (𝑥 + 1)2
−𝑥2
= 2𝑥 + 1
3 (𝑥 + 1)3−𝑥3 = 3𝑥2 + 3𝑥 + 1
︙ ︙

62. Difference Engine

63. Difference Engine

64. Difference Engine

65. Plan Diagram of the Analytical Engine

66. Plan Diagram of the Analytical Engine

67. Plan Diagram of the Analytical Engine

68. Plan Diagram of the Analytical Engine

69. Luigi Federico Menabrea
1809~1896

70. Luigi Federico Menabrea
1809~1896

71. Sir Charles Wheatstone
1802~1875

72. Sir Charles Wheatstone
1802~1875

73. Sir Charles Wheatstone
1802~1875

100. “Whenever any result is sought by its aid,
the question will then arise —
By what course of calculation
can these results be arrived at
by the machine
in the shortest time?”

101. “Whenever any result is sought by its aid,
the question will then arise —
By what course of calculation
can these results be arrived at
by the machine
in the shortest time?”

102. “Whenever any result is sought by its aid,
the question will then arise —
By what course of calculation
can these results be arrived at
by the machine
in the shortest time?”

103. “Whenever any result is sought by its aid,
the question will then arise —
By what course of calculation
can these results be arrived at
by the machine
in the shortest time?”

104. Every function can be calculated when
continued ad infinitum.
Operation is any process which alters the
mutual relation of two or more things.
Operations are homogeneous, but distributed
amongst different subjects of operation.
By what course of calculation can those results
be arrived in the shortest time?

105. Every function can be calculated when
continued ad infinitum.
Operation is any process which alters the
mutual relation of two or more things.
Operations are homogeneous, but distributed
amongst different subjects of operation.
By what course of calculation can those results
be arrived in the shortest time?

106. Every function can be calculated when
continued ad infinitum.
Operation is any process which alters the
mutual relation of two or more things.
Operations are homogeneous, but distributed
amongst different subjects of operation.
By what course of calculation can those results
be arrived in the shortest time?

107. Every function can be calculated when
continued ad infinitum.
Operation is any process which alters the
mutual relation of two or more things.
Operations are homogeneous, but distributed
amongst different subjects of operation.
By what course of calculation can those results
be arrived in the shortest time?

108. Every function can be calculated when
continued ad infinitum.
Operation is any process which alters the
mutual relation of two or more things.
Operations are homogeneous, but distributed
amongst different subjects of operation.
By what course of calculation can those results
be arrived in the shortest time?

109. Augustus De Morgan
1806~1871

110. Augustus De Morgan
1806~1871

111. Augustus De Morgan
1806~1871

112. De Morgan’s Laws
𝐴 ∪ 𝐵 = ҧ𝐴 ∩ ത𝐵
𝐴 ∩ 𝐵 = ҧ𝐴 ∪ ത𝐵
¬ 𝑃⋁𝑄 = ¬𝑃⋀¬𝑄
¬ 𝑃⋀𝑄 = ¬𝑃⋁¬𝑄
Set Theory Formal Language

113. De Morgan’s Laws
𝐴 ∪ 𝐵 = ҧ𝐴 ∩ ത𝐵
𝐴 ∩ 𝐵 = ҧ𝐴 ∪ ത𝐵
¬ 𝑃⋁𝑄 = ¬𝑃⋀¬𝑄
¬ 𝑃⋀𝑄 = ¬𝑃⋁¬𝑄
Set Theory Formal Language

114. George Boole
1815~1864

115. Boole's House and School
3 Pottergate in Lincoln

116. Boole's House and School
3 Pottergate in Lincoln

117. Boole's House and School
3 Pottergate in Lincoln

118. George Boole
1815~1864

119. Gottfried Wilhelm Leibniz
1646~1716

120. Sir William Rowan Hamilton
1805~1865

121. Sir William Rowan Hamilton
1805~1865

122. Sir William Rowan Hamilton
1805~1865

123. Sir William Rowan Hamilton
1805~1865

127. Joseph Hill’s letter (1851)
Recalling the meeting of Boole and Babbage

128. Joseph Hill’s letter (1851)
Recalling the meeting of Boole and Babbage
As Boole had discovered that means of reasoning
might be conducted by a mathematical process,
and Babbage had invented a machine
for the performance of mathematical work,
the two great men together seemed to have
taken steps towards the construction of
that great prodigy a Thinking Machine.

129. Joseph Hill’s letter (1851)
Recalling the meeting of Boole and Babbage
As Boole had discovered that means of reasoning
might be conducted by a mathematical process,
and Babbage had invented a machine
for the performance of mathematical work,
the two great men together seemed to have
taken steps towards the construction of
that great prodigy a Thinking Machine.

130. Joseph Hill’s letter (1851)
Recalling the meeting of Boole and Babbage
As Boole had discovered that means of reasoning
might be conducted by a mathematical process,
and Babbage had invented a machine
for the performance of mathematical work,
the two great men together seemed to have
taken steps towards the construction of
that great prodigy a Thinking Machine.

131. Joseph Hill’s letter (1851)
Recalling the meeting of Boole and Babbage
As Boole had discovered that means of reasoning
might be conducted by a mathematical process,
and Babbage had invented a machine
for the performance of mathematical work,
the two great men together seemed to have
taken steps towards the construction of
that great prodigy a Thinking Machine.

132. Claude Shannon
1916~2001

133. Claude Shannon
1916~2001

134. “… it just happened that
no one else was familiar
with both fields at the same time.”

135. “… it just happened that
no one else was familiar
with both fields at the same time.”

136. Switch representation
of AND, OR and NOT function

137. Switch representation
of AND, OR and NOT function

138. Switch representation
of AND, OR and NOT function

139. Howard Hathaway Aiken
1900~1973

140. Harvard Mark I

141. Harvard Mark I

142. Harvard Mark I

143. “… felt like Babbage was addressing
me personally from the past.”

144. “… felt like Babbage was addressing
me personally from the past.”

145. Harvard Architecture

146. Harvard Architecture

152. Grace Hopper
1906~1992

153. Grace Hopper
1906~1992

154. First computer bug while working on Mark II (1947)

155. First computer bug while working on Mark II (1947)

156. First computer bug while working on Mark II (1947)

157. John von Neumann
1903~1957

158. John von Neumann
1903~1957

159. John von Neumann
1903~1957

160. Von Neumann Architecture Harvard Architecture
One memory simplifies design. But
one bus acts as a bottleneck.
Two buses are expensive. Control
unit is tricky to develop.
One unified cache. Two separate cache.
Freedom to organize memory.
But could be error prone.
Free data memory not utilized.
H/W accelerated parallel execution
impossible. Only simulated by S/W.
Parallel access to data and
instruction.

161. Von Neumann Architecture Harvard Architecture
One memory simplifies design. But
one bus acts as a bottleneck.
Two buses are expensive. Control
unit is tricky to develop.
One unified cache. Two separate cache.
Freedom to organize memory.
But could be error prone.
Free data memory not utilized.
H/W accelerated parallel execution
impossible. Only simulated by S/W.
Parallel access to data and
instruction.

162. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

163. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

164. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

165. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

166. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

167. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

168. Boolean algebra gives operations of reasoning in
the symbolical language of calculus.
Prepositions are variables.
Yes/No in Boolean logic is now 1/0 in circuitry.
Variables are just signals before we chose to
look at it as variables.

169. Claude Shannon
1916~2001

170. “The fundamental problem of
communication is that of
reproducing at one point from a
message selected at another point.”

171. 𝑁 bits can
represent 2 𝑁
numbers.
=
𝐶 number of data can be
represented by log2 𝐶 bits.

172. 𝑁 bits can
represent 2 𝑁
numbers.
=
𝐶 number of data can be
represented by log2 𝐶 bits.

173. Shannon entropy for a biased coin

174. Shannon entropy for a biased coin

175. Shannon entropy for a biased coin

176. Shannon entropy for a biased coin

177. Shannon entropy for a biased coin

178. Shannon entropy for a biased coin

179. Shannon entropy for a biased coin

180. Shannon entropy for a biased coin

181. Shannon entropy for a biased coin

182. Shannon entropy for a biased coin

197. “Entropy is the average rate
at which information is produced
by a stochastic source of data.”

198. “Entropy is the average rate
at which information is produced
by a stochastic source of data.”

199. “Lower uniform probability means
more bits needed,
so more information.”

200. “Lower uniform probability means
more bits needed,
so more information.”

201. “Lower uniform probability means
more bits needed,
so more information.”

202. “Lower uniform probability means
more bits needed,
so more information.”

203. John von Neumann
1903~1957

204. “You should call it entropy, for two reasons.
In the first place your uncertainty function
has been used in statistical mechanics under that name,
so it already has a name.
In the second place, and more important,
nobody knows what entropy really is,
so in a debate you will always have the advantage.”

205. “You should call it entropy, for two reasons.
In the first place your uncertainty function
has been used in statistical mechanics under that name,
so it already has a name.
In the second place, and more important,
nobody knows what entropy really is,
so in a debate you will always have the advantage.”

206. “You should call it entropy, for two reasons.
In the first place your uncertainty function
has been used in statistical mechanics under that name,
so it already has a name.
In the second place, and more important,
nobody knows what entropy really is,
so in a debate you will always have the advantage.”

207. “You should call it entropy, for two reasons.
In the first place your uncertainty function
has been used in statistical mechanics under that name,
so it already has a name.
In the second place, and more important,
nobody knows what entropy really is,
so in a debate you will always have the advantage.”

208. The 2nd Law of Thermodynamics
Total entropy of an isolated system
can never decrease over time.

209. The 2nd Law of Thermodynamics
Total entropy of an isolated system
can never decrease over time.

210. The 2nd Law of Thermodynamics
Total entropy of an isolated system
can never decrease over time.

211. The 2nd Law of Thermodynamics
Total entropy of an isolated system
can never decrease over time.

212. Ludwig Boltzmann
1844~1906

213. Ludwig Boltzmann
1844~1906

214. Entropy of Gases

215. Entropy of Gases

216. Entropy of Gases

217. Entropy of Gases

218. Entropy of Gases

219. Entropy of Gases

220. Entropy of Gases

221. Entropy of Gases

222. Entropy of Gases

223. Entropy of Gases

224. Entropy of Gases

225. Entropy of Gases

226. Entropy of Gases

227. Entropy of Gases

228. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

229. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

230. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

231. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

232. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

233. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

234. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

235. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

236. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

237. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

238. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

239. 𝐻 𝑦, ො𝑦 = ෍
𝑖
𝑦𝑖 log
1
ෝ𝑦𝑖
= − ෍
𝑖
𝑦𝑖 log ෝ𝑦𝑖
Cross Entropy
Encoding the i-th symbol wrongly with log ෝ𝑦𝑖 bits.
Therefore always larger than entropy.

240. KL Divergence
Number of extra bits needed if wrongly encoded.
Minimizing KL divergence is same with minimizing cross entropy.
𝐾𝐿(𝑦| ො𝑦 = 𝐻 𝑦, ො𝑦 − 𝐻(𝑦, 𝑦) = ෍
𝑖
𝑦𝑖 log
𝑦𝑖
ෝ𝑦𝑖

241. KL Divergence
Number of extra bits needed if wrongly encoded.
Minimizing KL divergence is same with minimizing cross entropy.
𝐾𝐿(𝑦| ො𝑦 = 𝐻 𝑦, ො𝑦 − 𝐻(𝑦, 𝑦) = ෍
𝑖
𝑦𝑖 log
𝑦𝑖
ෝ𝑦𝑖

242. KL Divergence
Number of extra bits needed if wrongly encoded.
Minimizing KL divergence is same with minimizing cross entropy.
𝐾𝐿(𝑦| ො𝑦 = 𝐻 𝑦, ො𝑦 − 𝐻(𝑦, 𝑦) = ෍
𝑖
𝑦𝑖 log
𝑦𝑖
ෝ𝑦𝑖

243. KL Divergence
Number of extra bits needed if wrongly encoded.
Minimizing KL divergence is same with minimizing cross entropy.
𝐾𝐿(𝑦| ො𝑦 = 𝐻 𝑦, ො𝑦 − 𝐻(𝑦, 𝑦) = ෍
𝑖
𝑦𝑖 log
𝑦𝑖
ෝ𝑦𝑖

244. KL Divergence
Number of extra bits needed if wrongly encoded.
Minimizing KL divergence is same with minimizing cross entropy.
𝐾𝐿(𝑦| ො𝑦 = 𝐻 𝑦, ො𝑦 − 𝐻(𝑦, 𝑦) = ෍
𝑖
𝑦𝑖 log
𝑦𝑖
ෝ𝑦𝑖

245. “Entropy is the average rate
at which information is produced
by a stochastic source of data.”

246. “Entropy is the average rate
at which information is produced
by a stochastic source of data.”

247. “Entropy is the average rate
at which information is produced
by a stochastic source of data.”

248. Everything is information.

249. Everything is information.

256. Entropy is the average rate at which
information is produced by a stochastic
source of data.
Basically, everything is information.
We are the one who is operating it.

257. Entropy is the average rate at which
information is produced by a stochastic
source of data.
Basically, everything is information.
We are the one who is operating it.

258. Entropy is the average rate at which
information is produced by a stochastic
source of data.
Basically, everything is information.
We are the one who is operating it.

259. Entropy is the average rate at which
information is produced by a stochastic
source of data.
Basically, everything is information.
We are the one who is operating it.

260. Entropy is the average rate at which
information is produced by a stochastic
source of data.
Basically, everything is information.
We are the one who is operating it.

261. Entropy is the average rate at which
information is produced by a stochastic
source of data.
Basically, everything is information.
We are the one who is operating it.

262. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

263. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

264. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

265. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

266. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

267. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

268. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

269. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

270. Gödel's theorem and Systems of Logic Based on Ordinals.
Turing machine and Halting problem.
Church-Turing Conjecture and Lambda Calculus.
“Lady Lovelace’s Objection” and Turing test.

271. Will you give me
poetical philosophy, poetical science?

272. Will you give me
poetical philosophy, poetical science?

273. Will you give me
poetical philosophy, poetical science?