def section_7_2_1():
print("\nSection 7.2.1 Effect of hyper prior updates: first-trial probability")
df = pd.DataFrame(columns=['p_forecast_to_static', 'p_forecast_to_hyper']) # forces the columns to appear in this order
row_inputs = [
[{'ftp': 1 / 100}, {'ftp': 1 / 1000}],
[{'ftp': 1 / 10}, {'ftp': 1 / 100}],
[{'ftp': 1 / 10}, {'ftp': 1 / 100}, {'ftp': 1 / 1000}],
[{'ftp': 1 / 1000}, {'ftp': 1 / 10000}]
]
for input_list in row_inputs:
rowname = str([to_fraction_strings(i) for i in input_list])
for rule in input_list: # the same for every row in the table
rule['name'] = 'calendar'
rule['regime_start'] = 1956
rule['forecast_from'] = 2020
initial_weights = [1] * len(input_list)
datadict = functions.hyper_prior(rules=input_list, initial_weights=initial_weights)
datadict = to_percentage_strings(datadict)
df = df.append(pd.Series(name=rowname, data=datadict))
print(df)
def update_hyper_prior(self):
rules_dicts = []
weights = []
rules_attributes = [self.agi_impossible, self.lifetime, self.evolution]
# consider refactoring around this ugly code. would require some change to how hyper_prior takes args.
if self.calendar:
rules_attributes.append(self.calendar)
self.calendar.input_args['name'] = 'calendar'
if self.researcher:
rules_attributes.append(self.researcher)
self.researcher.input_args['name'] = 'res-year'
if self.comp_relative_res:
rules_attributes.append(self.comp_relative_res)
self.comp_relative_res.input_args['name'] = 'computation'
self.lifetime.input_args['name'] = 'computation'
self.lifetime.input_args['biohypothesis'] = 'lifetime'
self.evolution.input_args['name'] = 'computation'
self.evolution.input_args['biohypothesis'] = 'evolution'
if not rules_attributes:
return
for rule in rules_attributes:
rules_dicts.append(rule.input_args)
weights.append(rule.init_weight)
init_weights_normalized = np.asarray(weights)/sum(weights)
# We create the data here instead of passing a callable because it's far more efficient to generate the whole sequence in one call to functions.hyper_prior
hyper_results = {}
for year in range(2020,self.update_hyper_from + 1):
hyper_results[year] = {'p_forecast_to_hyper': 0}
forecast_years = generate_years_to_forecast((self.update_hyper_from,2100), fine_interval=3, coarse_interval=15)
hyper_results.update(functions.hyper_prior(rules_dicts,weights, forecast_years_explicit=forecast_years, return_sequence=True))
for i in range(len(rules_attributes)):
rules_attributes[i].weight2020 = to_percentage_strings(hyper_results[self.update_hyper_from + 1]['wts_forecast_from'][i])
rules_attributes[i].weight_init_normalized = to_percentage_strings(init_weights_normalized[i])
self.pAGI_hyper = hyper_results
self.pAGI_2036_hyper = to_percentage_strings(hyper_results[2036]['p_forecast_to_hyper'])
def section_7_3():
print("\n7.3 Allow some probability that AGI is impossible")
df = pd.DataFrame(columns=['pr2036 calendar-year', 'pr2036 20% impossible']) # forces this order
input_ftps = [
1 / 1000,
1 / 300,
1 / 200,
1 / 100,
1 / 50,
1 / 20,
1 / 10
]
rows = []
for input_ftp in input_ftps:
rows.append(
[
{'name': 'calendar', # Rule 1
'regime_start': 1956,
'ftp': input_ftp},
{'name': 'impossible'} # Rule 2
]
)
for row in rows:
rowname = to_fraction_strings(row[0]['ftp'])
datadict = functions.hyper_prior(row, initial_weights=[.8, .2])
datadict['pr2036 20% impossible'] = datadict.pop('p_forecast_to_hyper')
datadict['wt2020'] = datadict.pop('wts_forecast_from')[1]
datadict['pr2036 calendar-year'] = functions.four_param_framework_calendar(row[0]['ftp'])
datadict = to_percentage_strings(datadict)
df = df.append(pd.Series(name=rowname, data=datadict))
print(df)
def section_8():
print("\nSection 8: All-things considered judgement")
row_inputs = [
{'name':'low',
'rules':
[
{ # Rule 1
'name': 'calendar',
'virtual_successes': 0.5,
'regime_start': 1956,
'ftp': 1 / 1000,
},
{ # Rule 2
'name': 'res-year',
'virtual_successes': 0.5,
'regime_start': 1956,
'ftp_cal_equiv': 1 / 1000,
'g_exp': 4.3 / 100,
'g_act': 7 / 100,
},
{ # Rule 3
'name': 'impossible',
}
],
'weights': [50, 30, 20]
},
{'name':'central',
'rules':
[
{ # Rule 1
'name': 'calendar',
'regime_start': 1956,
'ftp': 1 / 300,
},
{ # Rule 2
'name': 'res-year',
'regime_start': 1956,
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'g_act': 11 / 100,
},
{ # Rule 3
'name': 'computation',
'spend2036': 'central',
'rel_imp_res_comp': 5,
'g_exp': 4.3 / 100,
'ftp_cal_equiv': 1 / 300,
'regime_start': 1956,
},
{ # Rule 4
'name': 'computation',
'spend2036': 'central',
'biohypothesis': 'lifetime',
'regime_start': 1956
},
{ # Rule 5
'name': 'computation',
'spend2036': 'central',
'biohypothesis': 'evolution'
},
{ # Rule 6
'name': 'impossible',
},
],
'weights': [
.30, # Rule 1
.30, # Rule 2
.05, # Rule 3
.10, # Rule 4
.15, # Rule 5
.10 # Rule 6
]
},
{'name':'high',
'rules':
[
{ # Rule 1
'name': 'calendar',
'regime_start': 2000,
'ftp': 1 / 100,
},
{ # Rule 2
'name': 'res-year',
'regime_start': 2000,
'ftp_cal_equiv': 1 / 100,
'g_exp': 4.3 / 100,
'g_act': 16 / 100,
},
{ # Rule 3
'name': 'computation',
'spend2036': 'aggressive',
'rel_imp_res_comp': 5,
'regime_start': 1956,
'g_exp': 4.3 / 100,
'ftp_cal_equiv': 1 / 100,
},
{ # Rule 4
'name': 'computation',
'spend2036': 'aggressive',
'biohypothesis': 'lifetime',
'regime_start': 1956,
},
{ # Rule 5
'name': 'computation',
'spend2036': 'aggressive',
'biohypothesis': 'evolution',
},
{ # Rule 6
'name': 'impossible',
},
],
'weights': [
.10, # Rule 1
.40, # Rule 2
.10, # Rule 3
.10, # Rule 4
.20, # Rule 5
.10, # Rule 6
]
}
]
print('P(AGI by 2036)')
for row in row_inputs:
datadict = functions.hyper_prior(row['rules'], initial_weights=row['weights'], forecast_to=2036)
print(row['name'],to_percentage_strings(datadict['p_forecast_to_hyper']))
print('P(AGI by 2100)')
for row in row_inputs:
datadict = functions.hyper_prior(row['rules'], initial_weights=row['weights'], forecast_to=2100)
print(row['name'], to_percentage_strings(datadict['p_forecast_to_hyper']))
central = row_inputs[1]
datadict = functions.hyper_prior(central['rules'], initial_weights=central['weights'], forecast_from=2025)
print("Central estimate conditional on no AGI by 2025:",to_percentage_strings(datadict['p_forecast_to_hyper']))
def appendix_9():
print("\nAppendix 9: AGI Impossible")
df = pd.DataFrame(columns=['pr2036 No Hyper', 'pr2036 20% impossible']) # forces order
rule2 = {'name': 'impossible'} # the same in every row
row_inputs = [
[
{'name': 'calendar',
'ftp': 1 / 300,
'regime_start': 1956},
rule2],
[
{'name': 'res-year',
'ftp_cal_equiv': 1 / 300,
'g_act': 11 / 100,
'g_exp': 4.3 / 100,
'regime_start': 1956},
rule2],
[
{'name': 'res-year',
'ftp_cal_equiv': 1 / 300,
'g_act': 21 / 100,
'g_exp': 4.3 / 100,
'regime_start': 1956},
rule2],
[
{'name': 'res-year',
'ftp_cal_equiv': 1 / 300,
'g_act': 21 / 100,
'g_exp': 4.3 / 100,
'regime_start': 2000},
rule2],
[
{'name': 'computation',
'spend2036': 'aggressive',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'rel_imp_res_comp': 5,
'regime_start': 1956},
rule2],
[
{'name': 'computation',
'spend2036': 'aggressive',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'rel_imp_res_comp': 1,
'regime_start': 1956},
rule2],
[
{'name': 'computation',
'spend2036': 'aggressive',
'biohypothesis': 'lifetime',
'regime_start': 1956},
rule2],
[
{'name': 'computation',
'spend2036': 'aggressive',
'biohypothesis': 'evolution'},
rule2],
[
{'name': 'computation-loguniform',
'spend2036': 'aggressive'},
rule2],
]
for row in row_inputs:
rowname = str(row)
datadict = functions.hyper_prior(row, initial_weights=[0.8, 0.2])
datadict['pr2036 20% impossible'] = datadict.pop('p_forecast_to_hyper')
datadict['wt2020'] = datadict.pop('wts_forecast_from')[1]
datadict['pr2036 No Hyper'] = functions.hyper_prior(row, initial_weights=[1, 0])['p_forecast_to_hyper']
datadict = to_percentage_strings(datadict)
df = df.append(pd.Series(name=rowname, data=datadict))
print(df)
def appendix_8():
print("\nAppendix 8: using a hyper-prior on different trial definitions")
df = pd.DataFrame(columns=['p_forecast_to_static', 'p_forecast_to_hyper']) # forces the columns to appear in this order
# First rule is the same in all rows
rule1 = {'name': 'calendar',
'regime_start': 1956,
'ftp': 1 / 300
}
row_inputs = [
[rule1,
{'name': 'res-year',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'g_act': 11 / 100,
'regime_start': 1956}
],
[rule1,
{'name': 'res-year',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'g_act': 21 / 100,
'regime_start': 1956},
],
[rule1,
{'name': 'res-year',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'g_act': 21 / 100,
'regime_start': 2000}
],
[rule1,
{'name': 'computation',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'rel_imp_res_comp': 5,
'regime_start': 1956,
'spend2036': 'aggressive'},
],
[rule1,
{'name': 'computation',
'ftp_cal_equiv': 1 / 300,
'g_exp': 4.3 / 100,
'rel_imp_res_comp': 1,
'regime_start': 1956,
'spend2036': 'aggressive'}
],
[rule1,
{'name': 'computation',
'biohypothesis': 'lifetime',
'regime_start': 1956,
'spend2036': 'aggressive'},
],
[rule1,
{'name': 'computation',
'biohypothesis': 'evolution',
'spend2036': 'aggressive'}
]
]
for input_list in row_inputs:
rowname = str(to_fraction_strings(input_list[1]))
initial_weights = [1] * len(input_list)
datadict = functions.hyper_prior(input_list, initial_weights)
# We only display the second weight
datadict['wt2020'] = datadict['wts_forecast_from'][1]
del datadict['wts_forecast_from']
datadict = to_percentage_strings(datadict)
df = df.append(pd.Series(name=rowname, data=datadict))
print(df)