Questionnaire Experts Results: Future Progress in Artificial Intelligence
Total submissions = 170
Comments
Please click here to view the comments →
Response rates
| 1 | PT-AI | 49% | 43 out of 88 |
| 2 | AGI | 65% | 72 out of 111 |
| 3 | EETN | 10% | 26 out of 250 |
| 4 | TOP100 | 29% | 29 out of 100 |
| Total | 31% | 170 out of 549 |
Research approaches
| Cognitive science | 47,9% |
| Integrated cognitive architectures | 42,0% |
| Algorithms revealed by computational neuroscience | 42,0% |
| Artificial neural networks | 39,6% |
| Faster computing hardware | 37,3% |
| Large-scale datasets | 35,5% |
| Embodied systems | 34,9% |
| Other method(s) currently completely unknown | 32,5% |
| Whole brain emulation | 29,0% |
| Evolutionary algorithms or systems | 29,0% |
| Other method(s) currently known to at least one investigator | 23,7% |
| Logic-based systems | 21,3% |
| Algorithmic complexity theory | 20,7% |
| No method will ever contribute to this aim | 17,8% |
| Swarm intelligence | 13,6% |
| Robotics | 4,1% |
| Bayesian nets | 2,6% |
When HLMI
Results sorted by groups of respondents
| PT-AI | Median | Mean | St. Dev. |
| 10% | 2023 | 2043 | 81 |
| 50% | 2048 | 2092 | 166 |
| 90% | 2080 | 2247 | 515 |
| AGI | Median | Mean | St. Dev. |
| 10% | 2022 | 2033 | 60 |
| 50% | 2040 | 2073 | 144 |
| 90% | 2065 | 2130 | 202 |
| EETN | Median | Mean | St. Dev. |
| 10% | 2020 | 2033 | 29 |
| 50% | 2050 | 2097 | 200 |
| 90% | 2093 | 2292 | 675 |
| TOP100 | Median | Mean | St. Dev. |
| 10% | 2024 | 2034 | 33 |
| 50% | 2050 | 2072 | 110 |
| 90%: | 2070 | 2168 | 342 |
| ALL | Median | Mean | St. Dev. |
| 10%: | 2022 | 2036 | 59 |
| 50%: | 2040 | 2081 | 153 |
| 90%: | 2075 | 2183 | 396 |
Results sorted by percentage steps
| 10% | Median | Mean | St. Dev. |
| PT-AI | 2023 | 2043 | 81 |
| AGI | 2022 | 2033 | 60 |
| EETN | 2020 | 2033 | 29 |
| TOP100 | 2024 | 2034 | 33 |
| ALL | 2022 | 2036 | 59 |
| 50% | Median | Mean | St. Dev. |
| PT-AI | 2048 | 2092 | 166 |
| AGI | 2040 | 2073 | 144 |
| EETN | 2050 | 2097 | 200 |
| TOP100 | 2050 | 2072 | 110 |
| ALL | 2040 | 2081 | 153 |
| 90% | Median | Mean | St. Dev. |
| PT-AI | 2080 | 2247 | 515 |
| AGI | 2065 | 2130 | 202 |
| EETN | 2093 | 2292 | 675 |
| TOP100 | 2070 | 2168 | 342 |
| ALL | 2075 | 2183 | 396 |
Clicks of the ‘never’ box. These answers did not enter in to the averages above.
| Never | no. | % |
| 10% | 2 | 1,2 |
| 50% | 7 | 4,1 |
| 90% | 28 | 16,5 |
From HLMI to superintelligence
| Median | Mean | St. Dev. | |
| Within 2 years | 10% | 19% | 24 |
| Within 30 years | 75% | 62% | 35 |
Median estimates on probability of super intelligence given HLMI in different groups of respondents:
| 2 years | 30 years | |
| AGI | 15% | 90% |
| EETN | 5% | 55% |
| TOP100 | 5% | 50% |
| PT-AI | 10% | 60% |
The impact of superintelligence
In terms of numbers:
| Extremely good | 26% |
| On balance good | 30% |
| More or less neutral | 20% |
| On balance bad | 15% |
| Extremely bad (existential catastrophe) | 19% |
Respondents Statistics
1. Concerning the above questions, how would you describe your own expertise? (0 = none, 9 = expert): Mean 5,85.
2. Concerning technical work in artificial intelligence, how would you describe your own expertise? (0 = none, 9 = expert): Mean 6,26.
| 3. What is your main home academic discipline? | |
| Biology/Physiology/Neurosciences | 3 |
| Computer Science | 107 |
| Engineering (non CS) | 6 |
| Mathematics/Physics | 10 |
| Philosophy | 20 |
| Psychology/Cognitive Science | 14 |
| Other academic discipline | 9 |
| None | 1 |