Penicillin and syphilis

*By Katja Grace, 2 February 2015*

[Penicillin](http://en.wikipedia.org/wiki/Penicillium) was a hugely important discovery. But was it a [discontinuity](http://aiimpacts.org/cases-of-discontinuous-technological-progress/ "Cases of Discontinuous Technological Progress") in the normal progression of research, or just an excellent discovery which followed a slightly less excellent discovery, and so on?

There are several senses in which penicillin might have represented a discontinuity. Perhaps its discovery was a huge leap in effectiveness, saving lives at a rate nobody thought possible. Or it might have been dramatically cheaper than expected. Or it could have heralded a step up in life expectancy, or a step down in disease prevalence.

We investigated this, to add to our list of cases of discontinuous technological progress, or alternatively to our new list of things we have investigated to potentially add to that list, but didn’t.

To make life easier, we focused on penicillin as used to remedy [syphilis](http://en.wikipedia.org/wiki/Syphilis) in particular. Our only conscious reason for choosing syphilis was that penicillin is used to cure it. It is also a disease against which penicillin was considered an important step forward.

First, was penicillin a huge step forward in effectiveness, compared to the usual progress? In the field of syphilis prevention, the recent competition was actually tough. A treatment for syphilis developed thirty years earlier was literally nicknamed ‘[magic bullet](http://jmvh.org/article/syphilis-its-early-history-and-treatment-until-penicillin-and-the-debate-on-its-origins/)’ and spawned a [nobel prize](http://www.nobelprize.org/nobel_prizes/medicine/laureates/1908/ehrlich-bio.html) as well as a frightening looking [movie](http://en.wikipedia.org/wiki/Dr._Ehrlich%27s_Magic_Bullet). More quantitatively, existing treatments at the time of penicillin’s introduction in the early 40s were apparently ‘successful’ for about [90%](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1047489/?page=1) of patients who took them, and it [doesn’t look like](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1047489/?page=3) penicillin did better initially. So probably penicillin didn’t jump ahead on effectiveness.

However a big difference appears to be hidden within ‘patients who took them’. The ‘magic bullet’ was an [arsenic compound, unstable in air](http://en.wikipedia.org/wiki/Arsphenamine), needing [frequent injections](http://jmvh.org/article/syphilis-its-early-history-and-treatment-until-penicillin-and-the-debate-on-its-origins/) for weeks, and producing risk to [life and limb](http://en.wikipedia.org/wiki/Arsphenamine). According to [the only paper I found with figures on this](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1047489/?page=1), about three quarter of patients ‘defected’ before receiving a ‘minimum curative dose’ of an arsenic and bismuth treatment, which suggests that while earlier cures were not obviously worse than dying of syphilis, they were also not obviously better (or else perhaps people dropped out when the severity of their side effects prohibited them from continuing). Penicillin [allowed](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1047489/?page=3) virtually everyone to get a minimum curative dose. So it’s possible that penicillin represented a discontinuity in this more inclusive success measure, but alas we don’t have the data to check. If penicillin really treated four times as many patients as recent precursors, and both cured most of them, and many would die otherwise, and little progress had happened on this measure since salvarsan, then penicillin would be worth at least thirty years of progress, and perhaps much more.

(I hear you wondering, if this magic bullet was so much better than its precursors, what on earth were the precursors? Breathing mercury was one. From before the 16th century until the discovery of salvarsan, ‘[mercury treatments](http://jmvh.org/article/syphilis-its-early-history-and-treatment-until-penicillin-and-the-debate-on-its-origins/)’ were common. Early mercury treatments included rubbing mercury into one’s body, and inhaling the mercury fumes, while later ones involved more sophisticated injections of mercury compounds).

I should point out that I’m confused by these apparently high defection rates. Syphilis has an untreated mortality rate of [8-58%](http://en.wikipedia.org/wiki/Epidemiology_of_syphilis), so defection from treatment would appear to be living pretty dangerously. This suggests I’m wrong about something, just so you know. Nonetheless, being right about everything doesn’t seem cost-effective here.

So far it seems like penicillin’s main advantage was in being less costly (including costs such as suffering and complications). Was this an abrupt change? It is hard to get figures for the inclusive costs of penicillin and its precursors, let alone a longer term trend to compare to. We can say that penicillin took around [eight days](http://jmvh.org/article/syphilis-its-early-history-and-treatment-until-penicillin-and-the-debate-on-its-origins/) at the very start, while other treatments took more than [twenty](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1047489/?page=2%7D). And penicillin seems to have been a lot less dangerous. However qualitatively speaking, salvarsan also [sounds](http://jmvh.org/article/syphilis-its-early-history-and-treatment-until-penicillin-and-the-debate-on-its-origins/) a lot less dangerous than mercury treatments. There were also further improvements in safety prior to penicillin, such as in neosalvarsan. So guessing qualitatively, penicillin might have been a few decades worth of previous progress in reducing costs, but probably not much more.

Even at the [height](http://www.nejm.org/action/showMediaPlayer?doi=10.1056%2FNEJMp1113569&aid=NEJMp1113569_attach_1&area=&) of syphilis, the disease was not common enough that resolving all of it in one year would produce a visible change to life expectancy, so we shan’t look at that.

Did syphilis rates decline abruptly? Both having syphilis and dying from it became much less common during the 1940s, which is probably [due](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1047489/?page=1) [in large part](http://en.wikipedia.org/wiki/Epidemiology_of_syphilis) to [antibiotics](http://jama.jamanetwork.com/article.aspx?articleid=183391) (see figures 1 and 2). Deaths from syphilis [declined by 98%](http://jama.jamanetwork.com/article.aspx?articleid=183391). However, as you can see, these were not abrupt changes. Things got modestly better every year for decades.

[![Syphilis infection in the US](http://aiimpacts.org/wp-content/uploads/2015/01/SyphilisUS2009.gif)](http://aiimpacts.org/wp-content/uploads/2015/01/SyphilisUS2009.gif)Figure 1: [Syphilis infection in the US](http://en.wikipedia.org/wiki/Epidemiology_of_syphilis#mediaviewer/File:SyphilisUS2009.gif)
[![Figure 2: syphilis deaths declined massively in the middle of last century.](http://aiimpacts.org/wp-content/uploads/2015/01/syphilis.png)](http://aiimpacts.org/wp-content/uploads/2015/01/syphilis.png)Figure 2: syphilis deaths [declined massively](http://jama.jamanetwork.com/article.aspx?articleid=768249) in the middle of last century.
In sum, it seems unlikely that there was abrupt progress on drug effectiveness conditional on completing the treatment, or on how many people had or died from syphilis. There may have been abrupt progress in overall costs or in drug effectiveness conditional on being offered the treatment, but these have been too hard to evaluate here.

So for now, we add it to the [list of things we checked](http://aiimpacts.org/discontinuous-progress-investigation/ "Discontinuous progress investigation"), but not the [list of things that were abrupt](http://aiimpacts.org/cases-of-discontinuous-technological-progress/ "Cases of Discontinuous Technological Progress"). The list of things we checked is part of a [larger page about this project](http://aiimpacts.org/discontinuous-progress-investigation/ "Discontinuous progress investigation"), in case you are curious. We also looked into the Haber process recently, but didn’t think it involved much discontinuity. We might blog about it at some point.

Some other interesting facts about syphilis and penicillin I learned:

* A pre-penicillin treatment for neurosyphilis [was](http://jmvh.org/article/syphilis-its-early-history-and-treatment-until-penicillin-and-the-debate-on-its-origins/) to give the patient malaria, because malaria mitigated the syphilis, and was considered more treatable than syphilis.
* Early on, penicillin was so valuable that doctors [recycled](http://www.lib.niu.edu/2001/iht810139.html) what hadn’t been metabolized by extracting it from patients’ urine.
* There is a [whole wikipedia page](http://en.wikipedia.org/wiki/History_of_penicillin) of previous occasions in history where people observed that mould prevented bacterial infection, more or less, but apparently didn’t follow up that much.

Penicillin and syphilis


Discontinuous progress investigation

*Published Feb 2, 2015; last substantially updated April 12 2020*

We have collected cases of discontinuous technological progress to inform our understanding of whether artificial intelligence performance is likely to undergo such a discontinuity. This page details our investigation.

We know of ten events that produced a robust discontinuity in progress equivalent to more than a century at previous rates in at least one interesting metric and 53 events that produced smaller or less robust discontinuities.

Details
-------

### Motivations

We are interested in learning [whether artificial intelligence is likely to see discontinuous progress in the lead-up](http://aiimpacts.org/likelihood-of-discontinuous-progress-around-the-development-of-agi/) to [human-level](http://aiimpacts.wpengine.com/human-level-ai/) capabilities, or to produce discontinuous change in any other socially important metrics (e.g. percent of global wealth possessed by a single entity, economic value of hardware). We are interested because we think this informs us about the plausibility of different future scenarios and about which research and other interventions are best now, and also because it is a source of disagreement, and so perhaps fruitful for resolution.[1](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-1-414 "For instance, if the development of advanced AI takes place in the context of a large discontinuity, then it is arguably more likely to involve large shifts in power, to take place sooner than predicted, to be surprising, to be disruptive, and to be dangerous. Also, our research should investigate questions such as how to prepare or be warned, rather than questions like when the present trajectories of AI progress will reach human-level capabilities. See likelihood of discontinuous progress around the development of AGI for more discussion.")
We seek to answer this question by investigating the prevalence and nature of discontinuities in other technological progress trends. The prevalence can then act as a baseline for our expectations about AI, which can be updated with any further [AI-specific evidence](https://aiimpacts.org/likelihood-of-discontinuous-progress-around-the-development-of-agi/), including that which comes from looking at the nature of other discontinuities (for instance, whether they arise in circumstances that are predicted by the [arguments](https://aiimpacts.org/likelihood-of-discontinuous-progress-around-the-development-of-agi/) that are made for predicting discontinuous progress in AI).

In particular, we want to know:

* How common are large discontinuities in metrics related to technological progress?
* Do any factors predict where such discontinuities will arise? (For instance, is it true that progress in a conceptual endeavor is more likely to proceed discontinuously? If there have been discontinuities in progress on a metric in the past, are further discontinuities more likely?)

As a secondary goal, we are interested in learning about the circumstances that have surrounded discontinuous technological change in the past, insofar as it may inform our expectations about the consequences of discontinuous progress in AI, should it happen.

### Methods

*Main article: [methodology for discontinuous progress investigation](http://aiimpacts.org/methodology-for-discontinuity-investigation/).*

To learn about the prevalence and nature of discontinuities in technological progress, we:

1. Searched for potential examples of discontinuous progress (e.g. ‘Eli Whitney’s cotton gin’) via our own understanding, online search, and suggestions from others.[2](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-2-414 "We thank ")
2. Chose specific metrics related to these potential examples (e.g. ‘cotton ginned per person per day’, ‘value of cotton ginned per cost’) and found historic data on progress on those metrics (usually in conjunction with choosing metrics, since metrics for which we can find data are much preferred). Some datasets we found already formed in one place, while others we collected ourselves from secondary sources.
3. Defined a ‘rate of past progress’ throughout each historic dataset (e.g. if the trend is broadly flat then gets steeper, we decide whether to call this exponential progress, or two periods of linear growth.)
4. Measured the discontinuity at each datapoint in each trend by comparing the progress at the point to the expected progress at that point based on the last datapoint and the rate of past progress (e.g. if the last datapoint five years ago was 600 units, and progress had been going at two units per year, and now a development took it to 800 units, we would calculate 800 units – 600 units = 200 units of progress = 100 years of progress in 5 years, for a 95 year discontinuity.)
5. Noted any discontinuities of more than ten years (‘moderate discontinuities’), and more than one hundred years (‘large discontinuities’)
6. Judged subjectively whether the discontinuity was a clear divergence from the past trend (i.e. the past trend was well-formed enough that the new point actually seemed well outside of plausible continuations of it).[3](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-3-414 "See this spreadsheet column for the judgments.")
7. Noted anything interesting about the circumstances of each discontinuity (e.g. the type of metric it was in, the events that appeared to lead to the discontinuity, the patterns of progress around it.)

Note that this is not an attempt to rigorously estimate the frequency of discontinuities in arbitrary trends, since we have not attempted to select arbitrary trends. We have instead selected trends we think might contain large discontinuities. Given this, it may be used as a loose upper bound on the frequency of discontinuities in similar technological trends.

It is likely that there are many minor errors in this collection of data and analysis, based on the rate at which we have found and corrected them, and the unreliability of sources used.

#### Definitions

Throughout, we use:

* **[Discontinuity](https://aiimpacts.org/methodology-for-discontinuity-investigation/#Discontinuity_calculation):** abrupt progress far above what one would have expected by extrapolation, measured in terms of how many years early the progress appeared relative to its expected date.
* **Moderate discontinuity:** 10-100 years of progress at previous rates occurred on one occasion
* **Large discontinuity:** at least 100 years of progress at previous rates occurred on one occasion
* **Substantial discontinuity:** a moderate or large discontinuity
* **[Robust discontinuity](https://aiimpacts.org/methodology-for-discontinuity-investigation/#Robust_discontinuities):** a discontinuity judged to involve a clear divergence from the past trend

### Summary figures

* We collected 21 case studies of potentially discontinuous technological progress (see [*Case studies*](https://aiimpacts.org/discontinuous-progress-investigation/?preview_id=414&preview_nonce=5c1b7b6d73&preview=true#Case_studies) below) and investigated 38 trends associated with them.
* 20 trends had a substantial discontinuity, and 15 had a large discontinuity.[4](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-4-414 "Recall that our trends were selected for being especially likely to contain discontinuities, so this is something like an upper bound on their frequency in trends in general. However some trends we investigated for fairly limited periods, so these may have contained more discontinuities than we found.")
* We found 88 substantial discontinuities, 39 of them large.
* These discontinuities were produced by 63 distinct events
* Ten events produced robust large discontinuities in at least one metric.

### Case studies

This is a list of areas of technological progress which we have tentatively determined to either involve discontinuous technological progress, or not. Note that we largely investigate cases that looked likely to be discontinuous.

#### Ship size

*Main article: [Historic trends in ship size](https://aiimpacts.org/historic-trends-in-ship-size/)*

Trends for ship tonnage (builder’s old measurement) and ship displacement for Royal Navy first rate line-of-battle ships saw eleven and six discontinuities of between ten and one hundred years respectively during the period 1637-1876, if progress is treated as linear or exponential as usual. There is a hyperbolic extrapolation of progress such that neither measurement sees any discontinuities of more than ten years.

We do not have long term data for ship size in general, however the SS *Great Eastern* seems to have produced around 400 years of discontinuity in both tonnage (BOM) and displacement if we use Royal Navy ship of the line size as a proxy, and exponential progress is expected, or 11 or 13 in the hyperbolic trend.

![](https://aiimpacts.org/wp-content/uploads/2019/10/Tonnage-1024x768.png)**Figure 1a:** Record tonnages for Royal Navy ships of the line
![](https://aiimpacts.org/wp-content/uploads/2019/10/DisplacementGE-1024x768.png)**Figure 1b:** Ship weight (displacement) over time, Royal Navy ships of the line and the *Great Eastern*, a discontinuously large civilian ship. The largest ship in the world three years prior to the *Great Eastern* was around 4% larger than the Ship of the Line of that time in this figure, so we know that the overall largest ship trend cannot have been much steeper than the Royal Navy ship of the line trend shown.
#### Image recognition

*Main article: [Effect of AlexNet on historic trends in image recognition](https://aiimpacts.org/effect-of-alexnet-on-historic-trends-in-image-recognition/)*

AlexNet did not represent a greater than 10-year discontinuity in fraction of images labeled incorrectly, or log or inverse of this error rate, relative to progress in the past two years of competition data.

![](https://lh4.googleusercontent.com/w-e-81fXsk_eLCXQ0C0dyoIf2526s-Gf42ZnC3eQ7iM3ZQfd6oy3V5yCpcgxjNDXbaqiN4EPMbfFh3h6tU7egni6eEBcWGMhRt-Ravk1-m5eMzZ27k27xVYvqfuTeC8p1iD6Oih4)**Figure 2:** Error rate (%) of ImageNet competitors from 2010 – 2012
#### Transatlantic passenger travel

*Main article: [Historic trends in transatlantic passenger travel](https://aiimpacts.org/historic-trends-in-transatlantic-passenger-travel/)*

The speed of human travel across the Atlantic Ocean has seen at least seven discontinuities of more than ten years’ progress at past rates, two of which represented more than one hundred years’ progress at past rates: Columbus’ second journey, and the first non-stop transatlantic flight.

![](http://aiimpacts.org/wp-content/uploads/2019/12/Passenger-1024x791.png)**Figure 3a:** Historical fastest passenger travel across the Atlantic (speeds averaged over each transatlantic voyage)
![](https://aiimpacts.org/wp-content/uploads/2019/12/PassengerZoom-1024x791.png) **Figure 3b:** Previous figure, shown since 1730
#### Transatlantic message speed

*Main article: [Historic trends in transatlantic message speed](https://aiimpacts.org/historic-trends-in-transatlantic-message-speed/)*

The speed of delivering a short message across the Atlantic Ocean saw at least three discontinuities of more than ten years before 1929, all of which also were more than one thousand years: a 1465-year discontinuity from Columbus’ second voyage in 1493, a 2085-year discontinuity from the first telegraph cable in 1858, and then a 1335-year discontinuity from the second telegraph cable in 1866.

![](http://aiimpacts.org/wp-content/uploads/2019/12/Time-to-send-a-140-character-message-across-the-Atlantic-Ocean.png) **Figure 4:** Average speed for message transmission across the Atlantic.
#### Long range military payload delivery

*Main article: [Historic trends in long range military payload delivery](https://aiimpacts.org/historic-trends-in-long-range-military-payload-delivery/)*

The speed at which a military payload could cross the Atlantic ocean contained six greater than 10-year discontinuities in 1493 and between 1841 and 1957:

|  |  |  |  |
| --- | --- | --- | --- |
| **Date** | **Mode of transport** | **Knots** | **Discontinuity size(years of progress at past rate)** |
| 1493 | Columbus’ second voyage | 5.8 | 1465 |
| 1884 | Oregon | 18.6 | 10 |
| 1919 | WWI Bomber (first non-stop transatlantic flight) | 106 | 351 |
| 1938 | Focke-Wulf Fw 200 Condor | 174 | 19 |
| 1945 | Lockheed Constellation | 288 | 25 |
| 1957 | R-7 (ICBM) | ~10,000 | ~500 |

![](https://aiimpacts.org/wp-content/uploads/2019/12/Speed-of-transatlantic-military-payload-delivery-4.png)**Figure 5:** Historic speeds of sending hypothetical military payloads across the Atlantic Ocean
#### Bridge spans

*Main article: [Historic trends in bridge span length](https://aiimpacts.org/historic-trends-in-bridge-span-length/)*

We measure eight discontinuities of over ten years in the history of longest bridge spans, four of them of over one hundred years, five of them robust as to slight changes in trend extrapolation.

![](http://aiimpacts.org/wp-content/uploads/2019/10/Historic-longest-bridge-spans-5-bridge-types-after-1800.png)**Figure 6:** Record bridge span lengths for five bridge types since 1800
#### Light intensity

*Main article: [Historic trends in light intensity](https://aiimpacts.org/historic-trends-in-light-intensity/)*

Maximum light intensity of artificial light sources has discontinuously increased once that we know of: argon flashes represented roughly 1000 years of progress at past rates.

![](http://aiimpacts.org/wp-content/uploads/2019/11/LightIntensityRecent-1024x791.png)**Figure 7:** Light intensity trend since 1800 (longer trend [available](https://aiimpacts.org/historic-trends-in-light-intensity/))
#### Book production

*Main article: [Historic trends in book production](https://aiimpacts.org/historic-trends-in-book-production/)*

The number of books produced in the previous hundred years, sampled every hundred or fifty years between 600AD to 1800AD contains five greater than 10-year discontinuities, four of them greater than 100 years. The last two follow the invention of the printing press in 1492.

The real price of books dropped precipitously following the invention of the printing press, but the longer term trend is sufficiently ambiguous that this may not represent a substantial discontinuity.

The rate of progress of book production changed shortly after the invention of the printing press, from a doubling time of 104 years to 43 years.

![](https://aiimpacts.org/wp-content/uploads/2019/10/BookProduction-1024x768.png)**Figure 8a:** Total book production in Western Europe
![](https://aiimpacts.org/wp-content/uploads/2019/10/RealPrice-1024x768.png)**Figure 8b:** Real price of books in England
#### Telecommunications performance

*Main article: [Historic trends in telecommunications performance](https://aiimpacts.org/historic-trends-in-telecommunications-performance/)*

There do not appear to have been any greater than 10-year discontinuities in telecommunications performance, measured as:

* bandwidth-distance product for all technologies 1840-2015
* bandwidth-distance product for optical fiber 1975-2000
* total bandwidth across the Atlantic 1956-2018

Radio does not seem likely to have represented a discontinuity in message speed.

![](https://media.springernature.com/original/springer-static/image/chp%3A10.1007%2F978-3-319-31903-2_8/MediaObjects/370011_1_En_8_Fig2_HTML.gif)**Figure 9a:** Growth in bandwidth-distance product across all telecommunications during 1840-2015 from Agrawal, 2016[5](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-5-414 "Agrawal, Govind P. 2016. “Optical Communication: Its History And Recent Progress”. Optics In Our Time, 177-199. Springer International Publishing. doi:10.1007/978-3-319-31903-2_8., https://link.springer.com/chapter/10.1007/978-3-319-31903-2_8")
![Fig.Â 8.8](https://media.springernature.com/lw785/springer-static/image/chp%3A10.1007%2F978-3-319-31903-2_8/MediaObjects/370011_1_En_8_Fig8_HTML.gif)**Figure 9b**:
Bandwidth-distance product in fiber optics alone, from Agrawal, 2016[6](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-6-414 "Agrawal, Govind P. 2016. “Optical Communication: Its History And Recent Progress”. Optics In Our Time, 177-199. Springer International Publishing. doi:10.1007/978-3-319-31903-2_8., https://link.springer.com/chapter/10.1007/978-3-319-31903-2_8") (Note: 1 Gb = 10^9 bits)
![](https://aiimpacts.org/wp-content/uploads/2019/10/CableBandwidth-1024x768.png)**Figure 9c:** Transatlantic cable bandwidth of all types. Pre-1980 cables were copper, post-1980 cables were optical fiber.
#### Cotton gins

*Main article:* [*Effect of Eli Whitney’s cotton gin on historic trends in cotton ginning*](http://aiimpacts.org/effect-of-eli-whitneys-cotton-gin-on-historic-trends-in-cotton-ginning/)

We estimate that Eli Whitney’s cotton gin represented a 10 to 25 year discontinuity in pounds of cotton ginned per person per day, in 1793. Two innovations in 1747 and 1788 look like discontinuities of over a thousand years each on this metric, but these could easily stem from our ignorance of such early developments. We tentatively doubt that Whitney’s gin represented a large discontinuity in the cost per value of cotton ginned, though it may have represented a moderate one.

![](http://aiimpacts.org/wp-content/uploads/2019/10/AllGinData-1024x768.png)**Figure 10:** Claimed cotton gin productivity figures, 1720 to modern day, coded by credibility and being records. The last credible best point before the modern day is an improved version of Whitney’s gin, two years after the original (the original features in the two high non-credible claims slightly earlier).
#### Altitude

*Main article: [Historic trends in altitude](https://aiimpacts.org/discontinuity-in-altitude-records/)*

Altitude of objects attained by man-made means has seen six discontinuities of more than ten years of progress at previous rates since 1783, shown below.

|  |  |  |  |
| --- | --- | --- | --- |
| **Year** | **Height (m)** | **Discontinuity (years)** | **Entity** |
| 1784 | 4000 | 1032 | Balloon |
| 1803 | 7280 | 1693 | Balloon |
| 1918 | 42,300 | 227 | [Paris gun](https://en.wikipedia.org/wiki/Paris_Gun) |
| 1942 | 85,000 | 120 | [V-2 Rocket](https://en.wikipedia.org/wiki/List_of_V-2_test_launches) |
| 1944 | 174,600 | 11 | [V-2 Rocket](https://en.wikipedia.org/wiki/List_of_V-2_test_launches) |
| 1957 | 864,000,000 | 35 | Pellets (after one day) |

![](http://aiimpacts.org/wp-content/uploads/2018/02/Altitudes-since-1750-3.png)**Figure 11:** Post-1750 altitudes of various objects, including many non-records. Whether we collected data for non-records is inconsistent, so this is not a complete picture of progress within object types. See image in detail [here](http://aiimpacts.org/wp-content/uploads/2018/02/Altitudes-since-1750-3.png).
#### Slow light

*Main article: [Historic trends in slow light technology](https://aiimpacts.org/historic-trends-in-slow-light-technology/)*

Group index of light appears to have seen discontinuities of 22 years in 1995 from Coherent Population Trapping (CPT) and 37 years in 1999 from EIT (condensate). Pulse delay of light over a short distance may have had a large discontinuity in 1994 but our data is not good enough to judge. After 1994, pulse delay does not appear to have seen discontinuities of more than ten years.

[![](http://aiimpacts.org/wp-content/uploads/2019/03/DelayngData4.png)](http://aiimpacts.org/wp-content/uploads/2019/03/DelayngData4.png)
**Figure 12:** Progress in pulse delay and group index. “Human speed” shows the rough scale of motion familiar to humans.

#### Particle accelerators

Main article: [Historic trends in particle accelerator performance](https://aiimpacts.org/particle-accelerator-performance-progress/)

None of particle energy, center-of-mass energy nor Lorentz factor achievable by particle accelerators appears to have undergone a discontinuity of more than ten years of progress at previous rates.

![](https://aiimpacts.org/wp-content/uploads/2019/11/ParticleEnergy-1024x768.png)**Figure 13a:** Particle energy in eV over time
![](https://aiimpacts.org/wp-content/uploads/2019/11/CMEnergy-1024x768.png)**Figure 13b:** Center-of-mass energy in eV over time
![](https://aiimpacts.org/wp-content/uploads/2019/10/gamma-vs.-Year.png)**Figure 13c:** Lorentz factor (gamma) over time.
#### Penicillin on syphilis

*Main article: [Penicillin and historic syphilis trends](https://aiimpacts.org/penicillin-and-historic-syphilis-trends/)*

Penicillin did not precipitate a discontinuity of more than ten years in deaths from syphilis in the US. Nor were there other discontinuities in that trend between 1916 and 2015.

The number of syphilis cases in the US also saw steep decline but no substantial discontinuity between 1941 and 2008.

On brief investigation, the effectiveness of syphilis treatment and inclusive costs of syphilis treatment do not appear to have seen large discontinuities with penicillin, but we have not investigated either thoroughly enough to be confident.

[![Syphilis infection in the US](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/SyphilisUS2009.gif)](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/SyphilisUS2009.gif)
**Figure 14a**: Syphilis—Reported Cases by Stage of Infection, United States, 1941–2009, according to the CDC[7](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-7-414 "From Figure 33 in Division of STD Prevention, “Sexually Transmitted Disease Surveillance 2009,” November 2010, https://web.archive.org/web/20170120091355/https://www.cdc.gov/std/stats09/surv2009-Complete.pdf.")
[![syphilis](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/syphilis.png)](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/syphilis.png)**Figure 14b:** Syphilis and AIDS mortality rates in the US during the 20th century.[8](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-8-414 "See table 4D in Gregory L. Armstrong, Laura A. Conn, and Robert W. Pinner, “Trends in Infectious Disease Mortality in the United States During the 20th Century,” JAMA 281, no. 1 (January 6, 1999): 61–66, https://doi.org/10.1001/jama.281.1.61.")
#### Nuclear weapons

*Main article:* [Effect of nuclear weapons on historic trends in explosives](https://aiimpacts.org/discontinuity-from-nuclear-weapons/)

Nuclear weapons constituted a ~7 thousand year discontinuity in energy released per weight of explosive (relative effectiveness).

Nuclear weapons do not appear to have clearly represented progress in the cost-effectiveness of explosives, though the evidence there is weak.

![](https://aiimpacts.org/wp-content/uploads/2019/11/RelativeEffectiveness-1024x768.png)**Figure 15:** Relative effectiveness of explosives, up to early nuclear bomb (note change to log scale)
#### High temperature superconductors

*Main article: [Historic trends in the maximum superconducting temperature](http://aiimpacts.org/historic-trends-in-the-maximum-superconducting-temperature/)*

The maximum superconducting temperature of any material up to 1993 contained four greater than 10-year discontinuities: A 14-year discontinuity with NbN in 1941, a 26-year discontinuity with LaBaCuO4 in 1986, a 140-year discontinuity with YBa2Cu3O7 in 1987, and a 10-year discontinuity with BiCaSrCu2O9 in 1987.

YBa2Cu3O7 superconductors seem to correspond to a marked change in the rate of progress of maximum superconducting temperature, from a rate of progress of .41 Kelvin per year to a rate of 5.7 Kelvin per year.

![](https://aiimpacts.org/wp-content/uploads/2020/02/Temperature-1024x768.png)**Figure 16:** Maximum superconducting temperate by material over time through 2015

#### Land speed records

*Main article: [historic trends in land speed records](https://aiimpacts.org/historic-trends-in-land-speed-records/)*

Land speed records did not see any greater-than-10-year discontinuities relative to linear progress across all records. Considered as several distinct linear trends it saw discontinuities of 12, 13, 25, and 13 years, the first two corresponding to early (but not first) jet-propelled vehicles.

The first jet-propelled vehicle just predated a marked change in the rate of progress of land speed records, from a recent 1.8 mph / year to 164 mph / year.

![](https://lh5.googleusercontent.com/eCOr_JdyKmcr8otgQ7ts2YzG5ZaY0iashNCOlPDbIEh5BsKevQvJQfqAlKuvi-rcTlw8uhCielPs80qxKpwWz5l6If8mVpuQnSfWh83sFnlw_XFwYIlmzAFjBNvk4eAIvMKcVzH3)**Figure 17:** Historic land speed records in mph over time. Speeds on the left are an average of the record set in mph over 1 km and over 1 mile. The red dot represents the first record in a cluster that was from a jet propelled vehicle. The discontinuities of more than ten years are the third and fourth turbojet points, and the last two points.
#### Chess AI

*Main article: [Historic trends in chess AI](http://aiimpacts.org/historic-trends-in-chess-ai/)*

The Elo rating of the best chess program measured by the Swedish Chess Computer Association did not contain any greater than 10-year discontinuities between 1984 and 2018.

![](https://aiimpacts.org/wp-content/uploads/2019/05/image-2-1024x633.png) **Figure 18:** Elo ratings of the best program on SSDF at the end of each year.
#### Flight airspeed

*Main article:* [*Historic trends in flight airspeed records*](http://aiimpacts.org/historic-trends-in-flight-airspeed-records/)

Flight airspeed records between 1903 and 1976 contained one greater than 10-year discontinuity: a 19-year discontinuity corresponding to the Fairey Delta 2 flight in 1956.

The average annual growth in flight airspeed markedly increased with the Fairey Delta 2, from 16mph/year to 129mph/year.

![](https://lh6.googleusercontent.com/8t3ONdcpzCrC_6h34Pb5XSS4h1MkCt8HAZ-FbzJYJpHykEOCPV4KDAk-3Bt0LGhTvY_iCXAzJotvOABhAq4QflopZdvbvvED4Y4-K4qiWAH1WjLO03YR143gayqc-L_RJpRy1KXS)**Figure 19:** Flight airspeed records over time
#### Structure heights

*Main article:* [*Historic trends in structure heights*](http://aiimpacts.org/discontinuity-from-the-burj-khalifa/)

Trends for tallest ever structure heights, tallest ever freestanding structure heights, tallest existing freestanding structure heights, and tallest ever building heights have each seen 5-8 discontinuities of more than ten years. These are:

* **Djoser and Meidum pyramids** (~2600BC, >1000 year discontinuities in all structure trends)
* Three cathedrals that were shorter than the all-time record (**Beauvais** **Cathedral** in 1569, **St Nikolai** in 1874, and **Rouen** **Cathedral** in 1876, all >100 year discontinuities in current freestanding structure trend)
* **Washington Monument** (1884, >100 year discontinuity in both tallest ever structure trends, but not a notable discontinuity in existing structure trend)
* **Eiffel Tower** (1889, ~10,000 year discontinuity in both tallest ever structure trends, 54 year discontinuity in existing structure trend)
* Two early skyscrapers: the **Singer Building** and the **Metropolitan Life Tower** (1908 and 1909, each >300 year discontinuities in building height only)
* **Empire State Building** (1931, 19 years in all structure trends, 10 years in buildings trend)
* **KVLY-TV mast** (1963, 20 year discontinuity in tallest ever structure trend)
* **Taipei 101** (2004, 13 year discontinuity in building height only)
* **Burj Khalifa** (2009, ~30 year discontinuity in both freestanding structure trends, 90 year discontinuity in building height trend)

![](http://aiimpacts.org/wp-content/uploads/2020/01/StructureRecord-1024x768.png)**Figure 20a:** All-time record structure heights, long term history
[![](http://aiimpacts.org/wp-content/uploads/2020/01/StructureRecordZoom.png)](http://aiimpacts.org/wp-content/uploads/2020/01/StructureRecordZoom.png)**Figure 20b:** All-time record structure heights, recent history
![](http://aiimpacts.org/wp-content/uploads/2020/01/RecordFreestandingStructure-1024x768.png)**Figure 20c:** All-time record freestanding structure heights, long term history
[![](http://aiimpacts.org/wp-content/uploads/2020/01/RecordFreeZoom.png)](http://aiimpacts.org/wp-content/uploads/2020/01/RecordFreeZoom.png)**Figure 20d:** All-time record freestanding structure heights, recent history
[![](http://aiimpacts.org/wp-content/uploads/2020/01/CurrentFreestandingStructure.png)](http://aiimpacts.org/wp-content/uploads/2020/01/CurrentFreestandingStructure.png)**Figure 20e:** At-the-time record freestanding structure heights, long term history
![](http://aiimpacts.org/wp-content/uploads/2020/01/CurrentFreeZoom-1024x768.png)**Figure 20f:** At-the-time record freestanding structure heights, recent history
[![](http://aiimpacts.org/wp-content/uploads/2020/01/TallestBuilding.png)](http://aiimpacts.org/wp-content/uploads/2020/01/TallestBuilding.png)**Figure 20g:** All-time record building heights, longer term history
![](http://aiimpacts.org/wp-content/uploads/2020/01/TallestBuildingZoom-1024x768.png)**Figure 20h:** All-time record building heights, longer term history
#### Breech loading rifles

*Main article: [Effects of breech loading rifles on historic trends in firearm progress](http://aiimpacts.org/effects-of-breech-loading-rifles-on-historic-trends-in-firearm-progress/)*

Breech loading rifles do not appear to have represented a discontinuity in firing rate of guns, since it appears that other guns had a similar firing rate already. It remains possible that breech loading rifles represent a discontinuity in another related metric.

#### Incomplete case studies

[This](https://aiimpacts.org/incomplete-case-studies-of-discontinuous-progress/) is a list of cases we have partially investigated, but insufficiently to include in this page.

### Extended observations

[This spreadsheet](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit?usp=sharing) contains summary data and statistics about the entire set of case studies, including all calculations for findings that follow.

#### Prevalence of discontinuities

* We investigated 38 trends in around 21 broad areas[9](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-9-414 "e.g. within the area of ‘structure height’ we investigated ‘all time tallest buildings, measured by architectural height’ and also ‘tallest at the time freestanding structures, measured by pinnacle height’")
* Of the 38 trends that we investigated, we found [20](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=I44) to contain at least one substantial discontinuity, and [15](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=K44) to contain at least one large discontinuity. (Note that our trends were selected for being especially likely to contain discontinuities, so this is something like an upper bound on their frequency in trends in general. However some trends we investigated for fairly limited periods, so these may have contained more discontinuities than we found.)
* Trends we investigated had in expectation [2.3](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=I50) discontinuities each, including [1](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=K50) large discontinuity each, and [0.37](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=M50) large robust discontinuities each (that we found–we did not necessarily investigate trends for the entirety of their history).
* We found [88](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=I2) substantial discontinuities, [20](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=M2:N2) of them robust, [14](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=M2) of them large and robust.
* These discontinuities were produced by 63 distinct events, 29 of them producing large discontinuities.
* The robust large discontinuities were produced by 10 events
* [32%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=M45) of trends we investigated saw at least one large, robust discontinuity (though note that trends were selected for being discontinuous, and were a very non-uniform collection of topics, so this could at best inform an upper bound on how likely an arbitrary trend is to have a large, robust discontinuity somewhere in a chunk of its history)
* [53%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=I45) of trends saw any discontinuity (including smaller and non-robust ones), and in expectation a trend saw [more than two](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=I50) of these discontinuities.
* On average, each trend had [0.001](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AG43) large robust discontinuities per year, or [0.002](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AG49) for those trends with at least one at some point[10](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-10-414 "Across trends where it seemed reasonable to compare, not e.g. where we only looked at a single development. Also note that this is the average of discontinuity/years ratios across trends, not the number of discontinuities across all trends divided by the number of years across all trends.")
* On average [1.4%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AE43) of new data points in a trend make for large robust discontinuities, or [4.9%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AE49) for trends which have one.
* On average [14%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AB43) of total progress in a trend came from large robust discontinuities (or [16%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AC43) of logarithmic progress), or [38%](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=1906429870&range=AB49) among trends which have at least one.
* Across all years of any metric we considered, the rate of discontinuities/year was around 0.02% (though note that this is heavily influenced by how often you consider thousands of years with poor data at the start).

Some fuller related data, from [spreadsheet](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=330500000&range=C5:G14):

|  | All discontinuities | Large | Robust | Robust large |
| Metrics checked | 38 | 38 | 38 | 38 |
| Discontinuity count | 88 | 39 | 20 | 14 |
| Trends exhibiting that type of discontinuity | 20 | 15 | 16 | 12 |
| Trends with 2+  discontinuities of that type | 14 | 10 | 4 | 2 |
| P(discontinuity|trend) | 0.53 | 0.39 | 0.42 | 0.32 |
| E(discontinuities per trend) | 2.3 | 1.0 | 0.5 | 0.4 |
| P(multiple discontinuities|trend) | 0.37 | 0.26 | 0.11 | 0.05 |
| P(multiple discontinuities|trend with at least one) | 0.70 | 0.67 | 0.25 | 0.17 |
| P(multiple discontinuities|trend with at least one, and enough search to find more) | 0.78 | 0.77 | 0.29 | 0.20 |

##### Nature of discontinuous metrics

We categorized each metric as one of:

1. ‘technical’: to do with basic physical parameters (e.g. light intensity, particle energy in particle accelerators)
2. ‘product’: to do with usable goods or services (e.g. cotton ginned per person per day, size of largest ships, height of tallest structures)
3. ‘industry’: to do with an entire industry rather than individual items (e.g. total production of books)
4. ‘societal’: to do with society at large (e.g. syphilis mortality)

We also categorized each metric as one of:

1. ‘feature’: a characteristic that is good, but not close to encompassing the purpose of most related efforts (e.g. ship size, light intensity)
2. ‘performance proxy’: approximates the purpose of the endeavor (e.g. cotton ginned per person per day, effectiveness of syphilis treatment)
3. ‘value proxy’: approximates the all-things-considered value of the endeavor (e.g. real price of books, cost-effectiveness of explosives)

Most metrics fell into ‘product feature’ (16) ‘technical feature’ (8) or ‘product performance proxy’ (6), with the rest (8) spread across the categories.

Here is what these trends are like ([from this spreadsheet](https://docs.google.com/spreadsheets/d/1iMIZ57Ka9-ZYednnGeonC-NqwGC7dKiHN9S-TAxfVdQ/edit#gid=600317213&range=AC72:AG84)):

|  |  |  |  |  |
| --- | --- | --- | --- | --- |
|  | product feature | technical feature | product performance proxy | rare categories |
| **All discontinuities** |  |  |  |  |
| number of discontinuities | 73 | 8 | 2 | 5 |
| number of trends | 16 | 8 | 6 | 8 |
| number of trends with discontinuities | 13 | 4 | 2 | 1 |
| discontinuities per trend | 4.6 | 1.0 | 0.3 | 0.6 |
| fraction of trends with discontinuity | 0.81 | 0.50 | 0.33 | 0.13 |
| **Large discontinuities** |  |  |  |  |
| number of large discontinuities | 32 | 3 | 0 | 4 |
| number of trends | 16 | 8 | 6 | 8 |
| number of trends with large discontinuities | 11 | 3 | 0 | 1 |
| large discontinuities per trend | 2.0 | 0.4 | 0.0 | 0.5 |
| fraction of trends with large discontinuity | 0.69 | 0.38 | 0.00 | 0.13 |

*Primary authors: Katja Grace, Rick Korzekwa, Asya Bergal*, *Daniel Kokotajlo.*

*Thanks to many other researchers whose work contributed to this project.*

*Thanks to Stephen Jordan, Jesko Zimmermann, Bren Worth, Finan Adamson, and others for suggesting potential discontinuities for this project in response to our 2015 bounty, and to many others for suggesting potential discontinuities since, especially notably Nuño Sempere, who conducted a detailed independent investigation into discontinuities in ship size and time to circumnavigate the world*[11](https://aiimpacts.org/discontinuous-progress-investigation/#easy-footnote-bottom-11-414 "Nuño Sempere. “Discontinuous Progress in Technological Trends.” Accessed March 8, 2021. https://nunosempere.github.io/rat/Discontinuous-Progress.html.")*.*

Notes
-----

Discontinuous progress investigation


List of Analyses of Time to Human-Level AI

This is a list of most of the substantial analyses of AI timelines that we know of. It also covers most of the arguments and opinions of which we are aware.

Details
-------

The list below contains substantial publically available analyses of when human-level AI will appear. To qualify for the list, an item must provide both a claim about when human-level artificial intelligence (or a similar technology) will exist, and substantial reasoning to support it. ‘Substantial’ is subjective, but a fairly low bar with some emphasis on detail, novelty, and expertise. We exclude arguments that AI is impossible, though they are technically about AI timelines.

### List

* [Good, Some future social repercussions of computers](http://www.tandfonline.com/doi/abs/10.1080/00207237008709398?journalCode=genv20) (1970) predicts 1993 give or take a decade, based roughly on the availability of sufficiently cheap, fast, and well-organized electronic components, or on a good understanding of the nature of language, and on the number of neurons in the brain.
* [Moravec, Today’s Computers, Intelligent Machines and Our Future](https://www.frc.ri.cmu.edu/~hpm/project.archive/general.articles/1978/analog.1978.html) (1978) projects that ten years later hardware equivalent to a human brain would be cheaply available, and that if software development ‘kept pace’ then machines able to think as well as a human would begin to appear then.
* [Solomonoff, The Time Scale of Artificial Intelligence: Reflections on Social Effects](http://iospress.metapress.com/content/h505v60q46562260/) (1985) estimates one to fifty years to a general theory of intelligence, then ten or fifteen years to a machine with general problem solving capacity near that of a human, in some technical professions.
* [Waltz, The Prospect for Building Truly Intelligent Machines](http://www.jstor.org/discover/10.2307/20025144?sid=21105674354083&uid=4&uid=2) (1988) predicts human-level hardware in 2017 and says the development of human-level AI might take another twenty years.
* [Vinge, The Coming Technological Singularity: How to Survive in the post-Human Era](http://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html) (1993) argues for less than thirty years from 1993, largely based on hardware extrapolation.
* [Eder, Re: The Singularity](http://www.aleph.se/Trans/Global/Singularity/singul.txt) (1993) argues for 2035 based on two lines of reasoning: hardware extrapolation to computation equivalent to the human brain, and hyperbolic human population growth pointing to a singularity at that time.
* [Yudkowsky, Staring Into the Singularity 1.2.5](http://www.yudkowsky.net/obsolete/singularity.html) (1996) presents calculation suggesting a singularity will occur in 2021, based on hardware extrapolation and a simple model of recursive hardware improvement.
* [Bostrom, How Long Before Superintelligence?](http://www.nickbostrom.com/superintelligence.html)(1997) argues that it is plausible to expect superintelligence in the first third of the 21st Century. In 2008 he added that he did not think the probability of this was more than half.
* [Bostrom, When Machines Outsmart Humans](http://www.nickbostrom.com/2050/outsmart.html)(2000) argues that we should take seriously the prospect of human-level AI before 2050, based on hardware trends and feasibility of uploading or software based on understanding the brain.
* [Kurzweil, The Singularity is Near](http://aiimpacts.org/kurzweil-the-singularity-is-near/ "Kurzweil, The Singularity is Near")[(pdf)](http://hfg-resources.googlecode.com/files/SingularityIsNear.pdf) (2005) predicts 2029, based mostly on hardware extrapolation and the belief that understanding necessary for software is growing exponentially. He also made a [bet](http://longbets.org/1/) with Mitchell Kapor, which he explains along with the bet and [here](https://web.archive.org/web/20110720061136/http://www.kurzweilai.net/a-wager-on-the-turing-test-why-i-think-i-will-win). Mitchell also explains his reasoning alongside the bet, though it nonspecific about timing to the extent that it isn’t clear whether he thinks AI will ever occur, which is why he isn’t included in this list.
* [Peter Voss, Increased Intelligence, Improved Life](http://archive.today/s45ly) [(video)](http://vimeo.com/33959613) (2007) predicts less than ten years and probably less than five, based on the perception that other researchers pursue unnecessarily difficult routes, and that shortcuts probably exist.
* [Moravec, The Rise of the Robots](http://www.scientificamerican.com/article/rise-of-the-robots/) (2009) predicts AI rivalling human intelligence well before 2050, based on progress in hardware, estimating how much hardware is equivalent to a human brain, and comparison with animals whose brains appear to be equivalent to present-day computers. Moravec made similar predictions in the 1988 book *Mind Children*.
* [Legg, Tick, Tock, Tick Tock Bing](http://www.vetta.org/2009/12/tick-tock-tick-tock-bing/) (2009) predicts 2028 in expectation, based on details of progress and what remains to be done in neuroscience and AI. He agreed with this prediction in [2012](http://www.vetta.org/2011/12/goodbye-2011-hello-2012/).
* [Allen, The Singularity Isn’t Near](http://aiimpacts.org/allen-the-singularity-isnt-near/ "Allen, The Singularity Isn’t Near")(2011) criticizes Kurzweil’s [prediction](http://aiimpacts.org/kurzweil-the-singularity-is-near/ "Kurzweil, The Singularity is Near") of a singularity around 2045, based mostly on disagreeing with Kurzweil on rates of brain science and AI progress.
* [Hutter, Can Intelligence Explode](http://www.hutter1.net/publ/singularity.pdf) (2012) uses a prediction of not much later than the 2030s, based on hardware extrapolation, and the belief that software will not lag far behind.
* [Chalmers (2010)](http://consc.net/papers/singularity.pdf) guesses that human-level AI is more likely than not this century. He points to several early estimates, but expresses skepticism about hardware extrapolation, based on the apparent algorithmic difficulty of AI. He argues that AI should be feasible within centuries (conservatively) based on the possibility of brain emulation, and the past success of evolution.
* [Fallenstein and Mennen](http://intelligence.org/files/PredictingAGI.pdf) (2013) suggest using a Pareto distribution to model time until we get a clear sign that human-level AI is imminent. They get a median estimate of about 60 years, depending on the exact distribution (based on an estimate of 60 years since the beginning of the field).
* [Drum, Welcome, Robot Overlords. Please Don’t Fire Us?](http://www.motherjones.com/media/2013/05/robots-artificial-intelligence-jobs-automation) (2013) argues for around 2040, based on hardware extrapolation.
* [Muehlhauser, When will AI be Created?](http://intelligence.org/2013/05/15/when-will-ai-be-created/) (2013) argues for uncertainty, based on surveys being unreliable, hardware trends being insufficient without software, and software being potentially jumpy.
* [Bostrom, Superintelligence](http://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies) (2014) concludes that ‘…it may be reasonable to believe that human-level machine intelligence has a fairly sizeable chance of being developed by mid-century and that it has a non-trivial chance of being developed considerably sooner or much later…’, based on expert surveys and interviews, such as [these](http://aiimpacts.wpengine.com/muller-and-bostrom-ai-progress-poll/ "Müller and Bostrom AI Progress Poll").
* [Sutton, Creating Human Level AI: How and When?](http://futureoflife.org/PDF/rich_sutton.pdf) (2015) places a 50% chance on human-level AI by 2040, based largely on hardware extrapolation and the view that software has a 1/2 chance of following within a decade of sufficient hardware.


The slow traversal of ‘human-level’

*By Katja Grace, 21 January 2015*

Once you have normal-human-level AI, [how long does it take](http://aiimpacts.wpengine.com/is-the-range-of-human-intelligence-small/ "The range of human intelligence") to get Einstein-level AI? We have seen that a common argument for ‘not long at all’ based on brain size [does not work](http://aiimpacts.wpengine.com/making-or-breaking-a-thinking-machine/ "Making or breaking a thinking machine") in a straightforward way, though a more nuanced assessment of the evidence might. Before we get to that though, let’s look at some more straightforward evidence (from [our new page](http://aiimpacts.wpengine.com/is-the-range-of-human-intelligence-small/ "The range of human intelligence") on the range of human intelligence).

In particular, let’s look at chess. AI can play superhuman-level chess, so we can see how it got there. And how it got there is via about four decades of beating increasingly good players, starting at beginners and eventually passing [Kasparov](http://en.wikipedia.org/wiki/Garry_Kasparov) (note that a beginner is something like [level F](http://en.wikipedia.org/wiki/Elo_rating_system#United_States_Chess_Federation_ratings) or below, which doesn’t make it onto this graph):

[![Figure 1: Chess AI progress compared to human performance, from Coles 2002. The original article was apparently written before 1993, so note that the right of the graph (after ‘now’) is imagined, though it appears to be approximately correct.](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/chess_progress.gif)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/chess_progress.gif)Figure 1: Chess AI progress compared to human performance, from [Coles 2002](http://www.drdobbs.com/parallel/computer-chess-the-drosophila-of-ai/184405171). The original article was apparently written before 1993, so note that the right of the graph (after ‘now’) is imagined, though it appears to be approximately correct.
Something similar is true in Go (where -20 on this graph is a good beginner score, and go bots are not yet superhuman, but getting close):

[![From Grace 2013.](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/gobothistory-copy-1024x743.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/gobothistory-copy.jpg)From [Grace 2013](http://intelligence.org/files/AlgorithmicProgress.pdf).
Backgammon and poker AI’s seems to have progressed similarly, though backgammon took about 2 rather than 4 decades (we will soon post more detailed descriptions of progress in board games).

Go, chess, poker, and backgammon are all played using different algorithms. But the underlying problems are sufficiently similar that they could easily all be exceptions.

Other domains are harder to measure, but seem basically consistent with gradual progress. Machine translation seems to be gradually moving through the range of human expertise, as does automatic driving. There are fewer clear cases where AI abilities took years rather than decades to move from subhuman to superhuman, and the most salient cases are particularly easy or narrow problems (such as arithmetic, narrow perceptual tasks, or easy board games).

If narrow AI generally traverses the relevant human range slowly, this suggests that general AI will take some time to go from minimum minimum wage competency to—well, at least to [AI researcher competency](http://en.wikipedia.org/wiki/Technological_singularity). If you combine many narrow skills, each progressing gradually through the human spectrum at different times, you probably wouldn’t end up with a much more rapid change in general performance. And it isn’t clear that a more general method should tend to progress faster than narrow AI.

However, we can point to ways that general AI might be different from board game AI.

Perhaps progress in chess and go has mostly been driven by hardware progress, while progress in general AI will be driven by algorithmic improvements or acquiring more training data.

Perhaps the kinds of algorithms people really use to think scale much better than chess algorithms. Chess algorithms only become [30-60 Elo points](http://intelligence.org/files/AlgorithmicProgress.pdf) stronger with each doubling of hardware, whereas a very rough calculation suggests human brains become more like 300 Elo points better per doubling in size.

In humans, [brain size has roughly a 1/3 correlation](http://en.wikipedia.org/wiki/Neuroscience_and_intelligence#Brain_size) with intelligence. Given that the standard deviation of brain size is about 10% of the size of the brain ([p. 39](https://books.google.com/books?id=KReeRcshYSMC&pg=PA40&lpg=PA40&dq=standard+deviation+of+human+brain+size&source=bl&ots=KnTcwf2Fuc&sig=awJuz6Z8zH0B9Uv-9Pq_iW9tlRQ&hl=en&sa=X&ei=1va_VIbaDMuwogSQsYLABQ&ved=0CDsQ6AEwBA#v=onepage&q=standard%20deviation%20of%20human%20brain%20size&f=false)), this suggests that a doubling of brain size leads to a relatively large change in chess-playing ability. On a log scale, a doubling is a 7 standard deviation change in brain size, which would suggest a ~2 standard deviation change in intelligence. It’s hard to know how this relates to chess performance, but in *Genius in Chess* Levitt [gives](http://www.jlevitt.dircon.co.uk/iq.htm) an unjustified estimate of 300 Elo points. This is what we would expect if intelligence were responsible for half of variation in performance (neglecting the lower variance of chess player intelligence), since a standard deviation of chess performance is [about 2000/7 ~ 300 Elo](http://en.wikipedia.org/wiki/Elo_rating_system). Each of these correlations is problematic but nevertheless suggestive.

If human intelligence in general scales much better with hardware than existing algorithms, and hardware is important relative to software, then AI based on an understanding of human intelligence may scale from sub-human to superhuman more quickly than the narrow systems we have seen. However these are both open questions.

*(Image: [The Chess Players](http://commons.wikimedia.org/wiki/File:Honor%C3%A9_Daumier_032.jpg), by Honoré Daumier)*

The slow traversal of ‘human-level’


Making or breaking a thinking machine

*By Katja Grace, 18 January 2015*

Here is a superficially plausible argument: the brains of the slowest humans are almost identical to those of the smartest humans. And thus—in the great space of possible intelligence—the ‘human-level’ band must be very narrow. Since all humans are basically identical in design—since you can move from the least intelligent human to the sharpest human with imperceptible changes—then artificial intelligence development will probably cross this band of human capability in a blink. It won’t stop on the way to spend years being employable but cognitively limited, or proficient but not promotion material. It will be superhuman before you notice it’s nearly human. And from our anthropomorphic viewpoint, from which the hop separating [village idiot and Einstein](http://lesswrong.com/lw/ql/my_childhood_role_model/) looks like most of the spectrum, this [might seem](https://intelligence.org/files/AIPosNegFactor.pdf) like shockingly sudden progress.

This whole line of reasoning is wrong.

It is true that human brains are very similar. However, this implies very little about the design difficulty of moving from the intelligence of one to the intelligence of the other artificially. The basic problem is that the smartest humans need not be better-designed — they could be better instantiations of the same design.

What’s the difference? Consider an analogy. Suppose you have a yard full of [rocket cars](http://en.wikipedia.org/wiki/Rocket_car). They all look basically the same, but you notice that their peak speeds are very different. Some of the cars can drive at a few hundred miles per hour, while others can barely accelerate above a crawl. You are excited to see this wide range of speeds, because you are a motor enthusiast and have been building your own vehicle. Your car is not quite up to the pace of the slowest cars in your yard yet, but you figure that since all those cars are so similar, once you get it to two miles per hour, it will soon be rocketing along.

If a car is slow because it is a rocket car with a broken fuel tank, that car will be radically simpler to improve than the first car you build that can go over 2 miles per hour. The difference is something like an afternoon of tinkering vs. [two](http://en.wikipedia.org/wiki/Nicolas-Joseph_Cugnot) [centuries](http://en.wikipedia.org/wiki/Rocket_car). This is intuitively because the broken rocket car already contains almost all of the design effort in making a fast rocket car. It’s not being used, but you know it’s there and how to use it.

Similarly, if you have a population of humans, and some of them are severely cognitively impaired, you shouldn’t get too excited about the prospects for your severely cognitively impaired robot.

Another way to see there must be something wrong with the argument is to note that humans can actually be arbitrarily cognitively impaired. Some of them are even dead. And the brain of a dead person can closely resemble the brain of a live person. Yet while these brains are again very similar in design, AI passed dead-human-level years ago, and this did not suggest that it was about to zip on past live-human-level.

Here is a different way to think about the issue. Recall that we were trying to infer from the range of human intelligence that AI progress would be rapid across that range. However, we can predict that human intelligence has a good probability of varying significantly, using only evolutionary considerations that are orthogonal to the ease of AI development.

In particular, if much of the variation in intelligence is from deleterious mutations, then the distribution of intelligence is more or less set by the equilibrium between selection pressure for intelligence and the appearance of new mutations. Regardless of how hard it was to design improvements to humans, we would always see this spectrum of cognitive capacities, so this spectrum cannot tell us about how hard it is to improve intelligence by design. (Though this would be different if the harm inflicted by a single mutation was likely to be closely related to the difficulty of designing an incrementally more intelligent human).

If we knew more about the sources of the variation in human intelligence, we might be able to draw a stronger conclusion. And if we entertain several possible explanations for the variation in human intelligence, we can still infer something; but the strength of our inference is limited by the prior probability that deleterious mutations on their own can lead to significant variation in intelligence. Without learning more, this probability shouldn’t be very low.

In sum, while the brain of an idiot is designed much like that of a genius, this does not imply that designing a genius is about as easy as designing an idiot.

We are still [thinking about this](http://aiimpacts.wpengine.com/is-the-range-of-human-intelligence-small/ "The range of human intelligence"), so now is a good time to tell us if you disagree. I even turned on commenting, to make it easier for you. It should work on all of the blog posts now.

[![](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/1024px-RAR2009_-_Rocket_Car-1024x539.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/1024px-RAR2009_-_Rocket_Car.jpg)[Rocket car](http://commons.wikimedia.org/wiki/File:RAR2009_-_Rocket_Car.jpg), photographed by Jon ‘ShakataGaNai’ Davis.
*(Top image: [One of the first cars](http://en.wikipedia.org/wiki/Nicolas-Joseph_Cugnot#mediaviewer/File:Nicholas-Cugnots-Dampfwagen.png), [1769](http://en.wikipedia.org/wiki/Nicolas-Joseph_Cugnot))*

Making or breaking a thinking machine


The range of human intelligence

***This page may be out-of-date. Visit the [updated version of this page](https://wiki.aiimpacts.org/doku.php?id=speed_of_ai_transition:range_of_human_performance:the_range_of_human_intelligence) on our [wiki](https://wiki.aiimpacts.org/doku.php?id=start).***

The range of human intelligence seems large relative to the space below it, as measured by performance on tasks we care about—despite the fact that human brains are extremely similar to each other.

Without knowing more about the sources of variation in human performance, however, we cannot conclude much at all about the likely pace of progress in AI: we are likely to observe significant variation regardless of any underlying facts about the nature of intelligence.

Details
-------

### Measures of interest

#### Performance

IQ is one measure of cognitive performance. Chess ELO is a narrower one. We do not have a general measure that is meaningful across the space of possible minds. However when people speak of ‘superhuman intelligence’ and the intelligence of animals they imagine that these can be meaningfully placed on some rough spectrum. When we say ‘performance’ we mean this kind of intuitive spectrum.

#### Development effort

We are especially interested in measuring intelligence by the difficulty of building a machine which exhibits that level of intelligence. We will not use a formal unit to measure this distance, but are interested in comparing the range between humans to distances between other milestones, such as that between a mouse and a human, or a rock and a mouse.

### Variation in cognitive performance

It is sometimes argued that humans occupy a very narrow band in the spectrum of cognitive performance. For instance, Eliezer Yudkowsky defends this rough schemata[1](https://aiimpacts.org/is-the-range-of-human-intelligence-small/#easy-footnote-bottom-1-191 "“My Childhood Role Model – LessWrong 2.0.” Accessed June 3, 2020. https://www.lesswrong.com/posts/3Jpchgy53D2gB5qdk/my-childhood-role-model.")—

[![sophVI_einstein copy](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/sophVI_einstein-copy1.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/sophVI_einstein-copy1.jpg)

—over these, which he attributes to others:

[![modsofVI_einstein copy](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/modsofVI_einstein-copy.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/modsofVI_einstein-copy.jpg)

[![](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/VI_einstein-copy.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/VI_einstein-copy.jpg)

Such arguments sometimes go further, to suggest that AI development effort needed to traverse the distance from the ‘village idiot’ to Einstein is also small, and so given that it seems so large to us, AI progress at around human level will [seem very fast](http://aiimpacts.org/likelihood-of-discontinuous-progress-around-the-development-of-agi/).

The landscape of performance is not easy to parameterize well, as there are many cognitive tasks and dimensions of cognitive ability, and no good global metric for comparison across different organisms. Nonetheless, we offer several pieces of evidence to suggest that the human range is substantial, relative to the space below it. We do not approach the topic here of how far above human level the space of possible intelligence reaches.

#### Low human performance on specific tasks

For most tasks, human performance reaches all of the way to the bottom of the possible spectrum. At the extreme, some comatose humans will fail at almost any cognitive task. Our impression is that people who are completely unable to perform a task are not usually isolated outliers, but that there is a distribution of people spread across the range from completely incapacitated to world-champion level. That is, for a task like ‘recognize a cat’, there are people who can only do slightly better than if they were comatose.

For our purposes we are more interested in where normal human cognitive performance fall relative to the worst and best possible performance, and the best human performance.

#### Mediocre human performance relative to high human performance

On many tasks, it seems likely that the best humans are many times better than mediocre humans, using relatively objective measures.[2](https://aiimpacts.org/is-the-range-of-human-intelligence-small/#easy-footnote-bottom-2-191 "In particular")

[Shockley (1957)](http://www.gwern.net/docs/1957-shockley.pdf) found that in science, the productivity of the top researchers in a laboratory was often at least ten times as great as the least productive (and most numerous) researchers. Programmers [purportedly](http://programmers.stackexchange.com/questions/179616/a-good-programmer-can-be-as-10x-times-more-productive-than-a-mediocre-one) vary by an order of magnitude in productivity, though this is debated. A third of people scored nothing in [this](https://math.mit.edu/news/spotlight/Putnam-2012-Results.pdf) Putnam competition, while someone scored 100. Some people have to work ten times harder to pass their high school classes than others.

Note that these differences are among people skilled enough to actually be in the relevant field, which in most cases suggests they are above average. Our impression is that something similar is true in other areas such as sales, entrepreneurship, crafts, and writing, but we have not seen data on them.

These large multipliers on performance at cognitive tasks suggest that the range between mediocre cognitive ability and genius is many times larger than the range below mediocre cognitive ability. However it is not clear that such differences are common, or to what extent they are due to differences in underlying general cognitive ability, rather than learning or non-cognitive skills, or a range of different cognitive skills that aren’t well correlated.

#### Human performance spans a wide range in other areas

In qualities other than intelligence, humans appear to span a fairly wide range below their peak levels. For instance, the fastest human runners are multiple times faster than mediocre runners ([twice as fast](http://www.telegraph.co.uk/sport/olympics/athletics/9450234/100m-final-how-fast-could-you-run-it.html) at a 100m sprint, [four](http://www.livestrong.com/article/551509-a-good-mile-rate-for-a-beginner-runner/) [times](http://en.wikipedia.org/wiki/Mile_run) as fast for a mile). Humans can vary in height by a factor of about [four](http://www.dailymail.co.uk/news/article-2832768/The-odd-couple-Shortest-man-21-5ins-meets-tallest-living-person-8ft-1in-outside-Houses-Parliament-Guinness-World-Record-Day.html), and commonly do by a factor of about 1.5. The most accurate painters are [hard to distinguish](http://twistedsifter.com/2012/04/15-hyperrealistic-paintings-that-look-like-photos-campos/) from photographs, while some painters are [arguably](http://www.psychologytoday.com/blog/psyched/201103/my-monkey-could-have-painted-really) hard to distinguish from monkeys, which are very easy to distinguish from photographs. These observations weakly suggest that the default expectation should be for humans to span a wide absolute range in cognitive performance also.

#### AI performance on human tasks

In domains where we have observed human-level performance in machines, we have seen rather gradual improvement across the range of human abilities. Here are five relevant cases that we know of:

1. **Chess:** human chess Elo ratings conservatively range from around [800 (beginner)](http://en.wikipedia.org/wiki/Elo_rating_system#United_States_Chess_Federation_ratings) to 2800 (world champion). The following figure illustrates how it took chess AI roughly forty years to move incrementally from 1300 to 2800.

[![chess_progress](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/chess_progress.gif)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/chess_progress.gif)Figure 1: Chess AI progress compared to human performance, from [Coles 2002](http://www.drdobbs.com/parallel/computer-chess-the-drosophila-of-ai/184405171). The original article was apparently written before 1993, so note that the right of the graph (after ‘now’) is imagined, though it appears to be approximately correct.

2. **Go:** Human go ratings [range](http://en.wikipedia.org/wiki/Go_ranks_and_ratings) from 30-20 kyu (beginner) to at least 9p (10p is a special title). Note that the numbers go downwards through kyu levels, then upward through dan levels, then upward through p(rofessional dan) levels. The following figure suggests that it took around 25 years for AI to cover most of this space (the top ratings seem to be [closer together](http://en.wikipedia.org/wiki/Go_ranks_and_ratings#Elo-like_rating_systems_as_used_in_Go) than the lower ones, though there are apparently [multiple systems](https://en.wikipedia.org/wiki/Go_ranks_and_ratings#Winning_probabilities) which vary).

[![From Grace 2013.](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/gobothistory-copy-1024x743.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2014/12/gobothistory-copy.jpg)Figure 2. From [Grace 2013](http://intelligence.org/files/AlgorithmicProgress.pdf).

3. **Checkers:**According to [Wikipedia’s timeline of AI](http://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence), a program was written in 1952 that could challenge a respectable amateur. In 1994 Chinook beat the second highest rated player ever. (In 2007 checkers was solved.) Thus it took around forty years to pass from amateur to world-class checkers-playing. We know nothing about whether intermediate progress was incremental however.

4. **Physical manipulation:** we have not investigated this much, but our impression is that robots are somewhere in the the fumbling and slow part of the human spectrum on [some](http://youtu.be/oD9DE0HjMM4) [tasks](https://www.youtube.com/watch?v=IBY4t8XxH7E), and that nobody expects them to reach the ‘normal human abilities’ part any time soon ([Aaron Dollar estimates](http://aiimpacts.wpengine.com/hanson-ai-expert-survey/ "Hanson AI Expert Survey") robotic grasping manipulation in general is less than one percent of the way to human level from where it was 20 years ago).

5. **Jeopardy**: AI appears to have taken two or three years to move from lower ‘champion’ level to surpassing world champion level (see [figure 9](https://www.aaai.org/ojs/index.php/aimagazine/article/view/2303/2165); Watson beat Ken Jennings in [2011](http://en.wikipedia.org/wiki/Watson_%28computer%29)). We don’t know how far ‘champion’ level is from the level of a beginner, but would be surprised if it were less than four times the distance traversed here, given the situation in other games, suggesting a minimum of a decade for crossing the human spectrum.

In all of these narrow skills, moving AI from low-level human performance to top-level human performance appears to take on the order of decades. This further undermines the claim that the range of human abilities constitutes a narrow band within the range of possible AI capabilities, though we may expect general intelligence to behave differently, for example due to smaller training effects.

On the other hand, most of the examples here—and in particular the ones that we know the most about—are board games, so this phenomenon may be less usual elsewhere. We have not investigated areas such as Texas hold ’em, arithmetic or constraint satisfaction sufficiently to add them to this list.

### What can we infer from human variation?

The brains of humans are nearly identical, [by comparison](http://lesswrong.com/lw/ql/my_childhood_role_model/) to the brains of other animals or to other possible brains that could exist. This might suggest that the engineering effort required to move across the human range of intelligences is quite small, compared to the engineering effort required to move from very sub-human to human-level intelligence (e.g. see [p21 and 29](https://intelligence.org/files/AIPosNegFactor.pdf), [p70](http://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies)). The similarity of human brains also suggest that the range of human intelligence is smaller than it seems, and its apparent breadth is due to anthropocentrism (see the same sources). According to these views, board games are an exceptional case–for most problems, it will not take AI very long to close the gap between “mediocre human” and “excellent human.”

However, we should not be surprised to find meaningful variation in the cognitive performance *regardless* of the difficulty of improving the human brain. This makes it difficult to infer much from the observed variations.

Why should we not be surprised? De novo deleterious mutations are introduced into the genome with each generation, and the prevalence of such mutations is determined by the [balance](http://en.wikipedia.org/wiki/Mutation%E2%80%93selection_balance) of mutation rates and negative selection. If de novo mutations significantly impact cognitive performance, then there must necessarily be significant selection for higher intelligence–and hence behaviorally relevant differences in intelligence. This balance is determined entirely by the mutation rate, the strength of selection for intelligence, and the negative impact of the average mutation.

You can often make a machine worse by breaking a random piece, but this does not mean that the machine was easy to design or that you can make the machine better by adding a random piece. Similarly, levels of variation of cognitive performance in humans may tell us very little about the difficulty of making a human-level intelligence smarter.

In the extreme case, we can observe that brain-dead humans often have very similar cognitive architectures. But this does not mean that it is easy to start from an AI at the level of a dead human and reach one at the level of a living human.

Because we should not be surprised to see significant variation–independent of the underlying facts about intelligence–we cannot infer very much from this variation. The strength of our conclusions are limited by the extent of our possible surprise.

By better understanding the sources of variation in human performance we may be able to make stronger conclusions. For example, if human intelligence is improving rapidly due to the introduction of  new architectural improvements to the brain, this suggests that discovering architectural improvements is not too difficult.  If we discover that spending more energy on thinking makes humans substantially smarter, this suggests that scaling up intelligences leads to large performance changes. And so on. Existing research in biology addresses the role of deleterious mutations, and depending on the results this literature could be used to draw meaningful inferences.

These considerations also suggest that brain similarity can’t tell us much about the “true” range of human performance. This isn’t too surprising, in light of the analogy with other domains. For example, although the bodies of different runners have nearly identical designs, the worst runners are not nearly as good as the best.

xxx[This background rate of human-range crossing is less informative about the future in scenarios where the increasing machine performance of interest is coming about in a substantially different way from how it came about in the past. For instance, it is sometimes hypothesized that major performance improvements will come from fast ‘recursive self improvement’, in which case the characteristic time scale might be much faster. However the scale of the human performance range (and time to cross it) relative to the area below the human range should still be informative.]

The range of human intelligence


Are AI surveys seeing the inside view?

*By Katja Grace, 15 January 2015*

An interesting thing about the [survey data](http://aiimpacts.wpengine.com/ai-timeline-surveys/ "AI Timeline Surveys") on timelines to human-level AI is the apparent incongruity between answers to ‘[when](http://aiimpacts.wpengine.com/muller-and-bostrom-ai-progress-poll/ "Müller and Bostrom AI Progress Poll") [will](http://aiimpacts.wpengine.com/agi-09-survey/ "AGI-09 Survey") [human-level](http://aiimpacts.wpengine.com/fhi-ai-timelines-survey/ "FHI Winter Intelligence Survey") [AI](http://aiimpacts.wpengine.com/kruel-ai-survey/ "Kruel AI Interviews") [arrive](http://aiimpacts.wpengine.com/bainbridge-survey/ "Bainbridge Survey")?’ and answers to ‘[how much of the way to human-level AI have we come recently?](http://aiimpacts.wpengine.com/hanson-ai-expert-survey/ "Hanson AI Expert Survey")‘

In particular, human-level AI [will apparently arrive in thirty or forty years](http://aiimpacts.wpengine.com/ai-timeline-surveys/ "AI Timeline Surveys"), while in the past twenty years most specific AI subfields have [apparently moved only five or ten percent](http://aiimpacts.wpengine.com/hanson-ai-expert-survey/ "Hanson AI Expert Survey") of the remaining distance to human-level AI, with little sign of acceleration.

Some possible explanations:

* The question about how far we have come has hardly been asked, and the small sample size has hit slow subfields, or hard-to-impress researchers, perhaps due to a different sampling of events.
* [Hanson](https://aiimpacts.org/feed/hanson.gmu.edu) (the only person who asked how far we have come) somehow inspires modesty or agreement in his audience. His survey methodology is conversational, and the answers do agree with [his own views](http://www.overcomingbias.com/2012/08/ai-progress-estimate.html).
* The ‘[inside view](http://www.mckinsey.com/insights/strategy/daniel_kahneman_beware_the_inside_view)‘ is overoptimistic: if you ask a person directly when their project will be done, they tend to [badly underestimate](http://en.wikipedia.org/wiki/Planning_fallacy). Taking the ‘[outside view](http://en.wikipedia.org/wiki/Reference_class_forecasting)‘ – extrapolating from similar past situations – helps to resolve these problems, and is more accurate. The first question invites the inside view, while the second invites the outside view.
* Different people are willing to answer the different questions.
* Estimating ‘how much of the way between where we were twenty years ago and human-level capabilities’ is hopelessly difficult, and the answers are meaningless.
* Estimating ‘when will we have human-level AI?’ is hopelessly difficult, and the answers are meaningless.
* When people answer the ‘how far have we come in the last twenty years?…’ question, they use a different scale to when they answer the ‘…and are we accelerating?’ question, for instance thinking of where we are as a fraction of what is left to do in the first case, and expecting steady exponential growth in that fraction, but not thinking of steady exponential growth as ‘acceleration’.
* AI researchers expect a small number of fast-growing subfields to produce AI with the full range of human-level skills, rather than for it to combine contributions from many subfields.
* Researchers have further information not captured in the past progress and acceleration estimates. In particular, they have reason to expect acceleration.

Since the two questions have so far yielded very different answers, it would be nice to check whether the different answers come from the different kinds of questions (rather than e.g. the small and casual nature of the [Hanson survey](http://aiimpacts.wpengine.com/hanson-ai-expert-survey/ "Hanson AI Expert Survey")), and to get a better idea of which kind of answer is more reliable. This might substantially change the message we get from looking at the opinions of AI researchers.

[Luke Muehlhauser](http://lukemuehlhauser.com/) and I have written before about [how to conduct a larger survey like Hanson’s](https://docs.google.com/document/d/1-eqYP1LumqZohBTGrujyPwj9q9WUx2c2leawzbaXrV0/edit#heading=h.kk4z5v8bo60l). One might also find or conduct experiments comparing these different styles of elicitation on similar predictions that can be sooner verified. There appears to be some contention over which method should be more reliable, so we could also start by having that discussion.

Are AI surveys seeing the inside view?


Event: Multipolar AI workshop with Robin Hanson

*By Katja Grace, 14 January 2015*

On Monday 26 January we will be holding a discussion on promising research projects relating to ‘[multipolar](http://lesswrong.com/lw/l9o/superintelligence_17_multipolar_scenarios/)‘ AI scenarios. That is, future scenarios where society persists in containing a large number of similarly influential agents, rather than a [single winner](http://www.nickbostrom.com/fut/singleton.html) who takes all. The event will be run in collaboration with [Robin Hanson](http://hanson.gmu.edu/), a leading researcher on the social consequences of [whole brain emulation](http://en.wikipedia.org/wiki/Mind_uploading).

The goal of the meeting will be to identify promising concrete research projects.

We will consider projects under various headings, for example:

> **Causal origins and probability of multipolar scenarios**
>
>
> * Collect past records of lumpiness of AI success
> * Survey military, business or academic projects which were particularly analogous to successful emulation or AI projects, to learn about the situations in which emulations or AI might appear.
> * Survey AI experts on the likelihood of AI emerging in the military, business or academia, and on the likely size of a successful AI project.
> * …
>
>
> **Consequences of multipolar scenarios**
>
>
> * Hanson’s project of detailing a default whole brain emulation scenario
> * How does the lumpiness of economic outcomes vary as a function of the lumpiness of origins?
> * Are there initial social institutions which might substantially influence longer term outcomes?
> * …
>
>
> **Applicability of broader AI safety insights to multipolar outcomes**
>
>
> * How useful are capability control methods, such as boxing, stunting, incentives, or tripwires in a multi-polar scenario?
> * How useful are motivation selection methods, such as direct specification, domesticity, indirect normatively, augmentation in a multipolar scenario?
> * Would selective pressures strongly favor the existence of goal-directed agents, in a multipolar scenario where a variety of AI designs are feasible?
> * …
>
>
>

###

### Details in brief

***Time*:**

2pm until 2-3 hours later

There will be an evening social event at 7pm in the same location, which workshop attendees are welcome to stay or return for. Some participants will go to dinner at a nearby restaurant in between.

***Date*:** Monday 26 January 2015

***Location*:**Private Berkeley residence. Detail available upon RSVP.

***RSVP*:** to katja.s.grace@gmail.com.

This is helpful, but not required (if you can deduce the location).

***Other things:***Tea, coffee and snacks provided.


Michie and overoptimism

*By Katja Grace, 12 January 2015*

We recently wrote about [Donald Michie’s](http://en.wikipedia.org/wiki/Donald_Michie) [survey](http://aiimpacts.wpengine.com/michie-survey/ "Michie Survey") on timelines to human-level AI. Michie’s survey is especially interesting because it was taken in 1972, which is three decades earlier than any other [surveys we know of](http://aiimpacts.wpengine.com/ai-timeline-surveys/ "AI Timeline Surveys") that ask about human-level AI.

Early AI predictions are renowned for being absurdly optimistic. And while the scientists in Michie’s survey had had a good decade and a half since the Dartmouth Conference to observe the lack of AI, they were still pretty early. Yet they don’t seem especially optimistic. Their predictions were so far in the future that almost three quarters of them still haven’t been demonstrated wrong.

And you might think computer scientists in the early 70s would be a bit more optimistic than contemporary forecasters, given that they hadn’t seen so many decades of research not produce human-level AI. But the median estimate they gave for how many years until AI was further out than those given in [almost all](http://aiimpacts.wpengine.com/ai-timeline-surveys/ "AI Timeline Surveys") surveys since (fifty years vs. thirty to forty). The survey doesn’t look like it was very granular – there appear to be only five options – so maybe a bunch of people would have said thirty-five years, and rounded up to fifty. Still, their median expectations don’t look like they were substantially more optimistic (in terms of time to AI) than present-day ones.

In terms of absolute dates, the Michie participants’ median fifty-year choice of 2022 is still considered [at least 10% likely](http://aiimpacts.wpengine.com/ai-timeline-surveys/ "AI Timeline Surveys") to give rise to human-level AI by recent survey participants, some four decades later.

It’s not like anyone said that all early predictions were embarrassingly optimistic though. Maybe Michie’s computer scientists were outliers? Perhaps, but if everyone else whose predictions we know of disagreed with them, it would be those others who were the outliers: Michie’s survey has sixty-three respondents, whereas [the MIRI dataset](http://lesswrong.com/lw/e79/ai_timeline_prediction_data/) contains only eleven other predictions made before 1980 (it looks like twelve, but the other interesting looking survey attributed to Firschein and Coles appears to be an accidental duplicate of the Michie survey, which Firschein and Coles mention in their paper). Sixty-three is more than all the predictions in the [MIRI dataset](http://lesswrong.com/lw/e79/ai_timeline_prediction_data/) until the 2005 Bainbridge survey.

AI researchers may have been extremely optimistic in the very early days (the [MIRI dataset](http://lesswrong.com/lw/e79/ai_timeline_prediction_data/) suggests this, though it consists of public statements, which tend to be more optimistic anyway). However, it doesn’t seem to have taken AI researchers long to move to something like contemporary views. It looks like they didn’t just predict ‘twenty years’ every year since [Dartmouth](http://en.wikipedia.org/wiki/Dartmouth_Conferences).

[![Survey results, as shown in Michie's paper.](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/michietimelines-copy-1-300x225.jpg)](http://aiimpacts.wpengine.com/wp-content/uploads/2015/01/michietimelines-copy-1.jpg)Survey results, as shown in Michie’s paper [(pdf download)](https://saltworks.stanford.edu/assets/cf501kz5355.pdf).
Thanks to Luke Muehlhauser for [suggesting](http://lukemuehlhauser.com/some-studies-which-could-improve-our-strategic-picture-of-superintelligence/) this line of inquiry.

*(Image: “[Donald Michie teaching](http://commons.wikimedia.org/wiki/File:Donald_Michie_teaching.jpg#mediaviewer/File:Donald_Michie_teaching.jpg)” by Petermowforth)*

Michie and overoptimism