AI Impacts

AI Impacts 2020 review

Tue, 22 Dec 2020 06:24:04 GMT

Updated Dec 21, 2020

This is a list of work done at AI Impacts published in 2020.

Major projects

Discontinuities project

Primary authors: Katja Grace, Richard Korzekwa, Asya Bergal, Daniel Kokotajlo
Main page: Discontinuous progress investigation
Blog post: Discontinuous progress in history: an update

Evolution engineering comparison

Primary authors: Ronny Fernandez
Main page: How energy efficient are human-engineered flight designs relative to natural ones?

Other pages added in 2020: Energy efficiency of monarch butterfly flight, Energy efficiency of wandering albatross flight, Energy efficiency of paramotors, Energy efficiency of The Spirit of Butt’s Farm, Energy efficiency of MacCready Gossamer Albatross, Energy efficiency of Boeing 747-400, Energy efficiency of Airbus A320, Energy efficiency of North American P-51 Mustang, Energy efficiency of Vickers Vimy plane, Energy efficiency of Wright model B, Energy efficiency of Wright Flyer

Time to cross human performance range

Primary authors: Richard Korzekwa
Pages added in 2020:

Historic hardware trends

Primary authors: Asya Bergal
Pages added in 2020:

Interviews on plausibility of AI safety by default

Primary authors: Asya Bergal, Robert Long
Main page: Interviews on plausibility of AI safety by default
Blog post: Takeaways from safety by default interviews

Shallow survey of prescient actions

Primary authors: Richard Korzekwa
Main page: Preliminary survey of prescient actions

Other work

Pages added in 2020

AI Impacts key questions of interest — Katja Grace
Was the industrial revolution a drastic departure from historic trends? — Katja Grace
Surveys on fractional progress towards HLAI — Asya Bergal
Precedents for economic n-year doubling before 4n-year doubling — Daniel Kokotajlo
Resolutions of mathematical conjectures over time — Asya Bergal

Blog posts added in 2020

Misalignment and misuse: whose values are manifest? — Katja Grace
Automated intelligence is not AI — Katja Grace
Relevant pre-AGI possibilities — Daniel Kokotajlo, Asya Bergal
Description vs simulated prediction — Richard Korzekwa
Atari early — Katja Grace
Three kinds of competitiveness — Daniel Kokotajlo
AGI in a vulnerable world — Asya Bergal
Cortes, Pizarro, and Afonso as precedents for takeover — Daniel Kokotajlo

AI Impacts key questions of interest

Thu, 17 Dec 2020 19:10:43 GMT

Updated Dec 17, 2020

This is a list of questions that AI Impacts focuses on answering.

Details

We are interested in understanding how the development of advanced AI will proceed and how it may affect humanity, especially insofar as these are relevant to efforts to improve the outcomes. This is a list of questions within this topic that we currently consider particularly important to answer.

List

AI RISK: What type and degree of risk will be posed to humanity by advanced AI systems?
CHARACTER: What will early advanced AI systems be like?
- ARCHITECTURE: What types of algorithms will advanced AI systems use?
- AGENCY: Will the bulk of advanced AI systems be in the form of ‘agents’? (If so, in what sense? Will they pursue ‘goals’? Can we say anything about the nature the goals or the pursuit?)
- PRICE: How much will the first human-level AI systems cost?
TIMELINES: When will human-level AI be developed? (When will other important AI milestones take place?)
TAKE-OFF SPEED: How rapid is the development of AI likely to be near human-level?
- DISCONTINUITY: Will there be abrupt progress in AI development at around human-level performance?
- INTELLIGENCE EXPLOSION: How much will AI development be accelerated by feedback from AI-based automation of the process?
PRE-AI DEVELOPMENTS: What developments will take place before advanced AI is developed?
- PATHS TO HLAI: By what methods is advanced AI likely to come about? (e.g. will human-level AI be developed via brain emulation before it is developed via machine learning? Will neuroscientific understanding play a large role in development?)
- CONTEMPORARY EVENTS: How will the world be relevantly different at the time that advanced AI is developed?
- WARNING SIGNS: Should we expect advance notice of disruptive change from AI? (What would it look like?)
POST-AI SOCIETY: If advanced AI transforms the world, what will the world look like afterwards? (e.g. What will the economic impacts be? Will humans flourish? What roles will AI systems play in society?)
ACTIONS: What can we say about the impact of contemporary choices on long-term outcomes?

How energy efficient are human-engineered flight designs relative to natural ones?

Thu, 10 Dec 2020 22:48:00 GMT

Updated Dec 10, 2020

Among two animals and nine machines:

In terms of mass⋅distance/energy, the most efficient animal was 2-8x more efficient than the most efficient machine. All entries fell within two orders of magnitude.
In terms of distance/energy, the most efficient animal was 3,000-20,000x more efficient than the most efficient machine. Both animals were more efficient than all machines. Entries ranged over more than eight orders of magnitude.

Details

Background

This case study is part of research that intends to compare the performance of human engineers and natural evolution on problems where both have developed solutions. The goal of this is to inform our expectations about the performance of future artificial intelligence relative to biological minds.

Metrics

We consider two metrics:

Distance per energy used (meters / kilojoule).
Mass times distance per energy used (kilograms⋅meters / joule).

These operationalize the problem of flight into two more specific problems. There are many other aspects of flight performance that one could measure, such as energy efficiency of acceleration in a straight line, turning, hovering, vertical acceleration, vertical distance, landing, taking off, time flying per energy, and our same measures with fewer or further restrictions on acceptable entries. For instance, we might look at the problem of flying with flapping wings, or without the restriction that the solutions we consider are heavier than air and self powered.

We did not require that the flight of an entry be constantly powered. Solutions that spend some time gliding as well as some time using powered flight were allowed. Both albatrosses and butterflies use air currents to fly further.¹ The energy gains from these techniques were not included in the final score, and entries were not penalized for spending a larger fraction of time gliding. It seems likely that paramotor pilots use similar techniques, since paramotors are well suited to gliding (being paragliders with propeller motors strapped to the backs of their pilots). Our energy efficiency estimate for the paramotor came from a record breaking distance flight in which the quantity of available fuel was limited, and so it is likely that some gliding was used to increase the distance traveled as much as possible.

When multiple input values could have been used, such as the takeoff weight and the landing weight, or different estimates for the energetic costs of different kinds of flight for the Monarch butterfly, we generally calculated a high and a low estimate, taking the most optimistic and pessimistic inputs respectively. In all cases, the resulting best and worst estimates differed by less than a factor of ten.

Selection of case studies

We selected case studies informally, according to judgments about possible high energy efficiencies, and with an eye to exploring a wider range of case studies.

We started by looking at the Boeing 747-400 plane, the Wandering Albatross, and the Monarch Butterfly. We chose the animals for both being known for their abilities to fly long distances, and for both having fairly different body plans.

All three scored surprisingly similarly on distance times weight per energy (details below). This prompted us to look for engineered solutions that were optimized for fuel efficiency. To that end, we looked at paramotors and record breaking flying machines. In the latter category, we found the MacCready Gassomer Albatross, which was a human powered flying device that crossed the English Channel, and the Spirit of Butts’ Farm, which was a model airplane that crossed the Atlantic on one gallon of gasoline.

For reasons that are now obscure, we also included a number of different planes.

We would have liked to include microdrones, since they are different enough from other entries that they might be unusually efficient. However we did not find data on them.

Case studies

These are the full articles calculating the efficiencies of different flying machines and animals:

Summary results

Results are available in Table 1 below, and in this spreadsheet. Figures 1 and 2 below illustrate the equivalent questions of how far each of these animals and machines can fly, given either the same amount of fuel energy, or fuel energy proportional to their body mass.

Name	natural or human-engineered		kg⋅m/J			m/kJ
		worst	mean	best	worst	mean	best
Monarch Butterfly	natural	0.065	0.21	0.36	100000	350000	600000
Wandering Albatross	natural	1.4	2.2	3	240	240	240
The Spirit of Butt’s Farm	human-engineered	0.086	0.12	0.16	32	32	32
MacGready Gossamer Albatross	human-engineered	0.19	0.32	0.46	2	3.3	4.6
Paramotor	human-engineered	0.058	0.079	0.1	0.36	0.36	0.36
Wright model B	human-engineered	0.036	0.078	0.12	0.1	0.16	0.21
Wright Flyer	human-engineered	0.022	0.042	0.061	0.080	0.13	0.18
North American P-51 Mustang	human-engineered	0.25	0.38	0.5	0.073	0.083	0.092
Vickers Vimy	human-engineered	0.081	0.17	0.25	0.025	0.038	0.05
Airbus A320	human-engineered	0.33	0.47	0.61	0.0078	0.0078	0.0078
Boeing 747-400	human-engineered	0.39	0.61	0.83	0.0021	0.0021	0.0021

Table 1: Energy efficiency of flight for a variety of natural and man-made flying entities.

Figure 1: If you give each animal or machine energy proportional to its weight, how far can it fly?

On mass⋅distance/energy, evolution beats engineers, but they are relatively evenly matched: the albatross (1.4-3.0 kg.m/J) and the Boeing 747-400 (0.39-0.83 kg.m/J) are the best in the natural and engineered classes respectively. Thus the best natural solution we found was roughly 2x-8x more efficient than the human-engineered one.² We found several flying machines more efficient on this metric than the monarch butterfly.

Figure 2: How far animals and machines can fly on the same amount of energy. Note that the vertical axis is log scaled, unlike that of Figure 1, so smaller looking differences are in fact much larger: over eight orders of magnitude (vs less than two in Figure 1).

On distance/energy, the natural solutions have a much larger advantage. Both are better than all man-made solutions we considered. The best natural and engineered solutions respectively are the monarch butterfly (100,000-600,000 m/kJ) and the Spirit of Butts’ Farm (32 m/kJ), for roughly a 3,000x to 20,000x advantage to natural evolution.

Interpretation

We take this as weak evidence about the best possible distance/energy and distance.mass/energy measures achievable by human engineers or natural evolution. One reason for this is that this is a small set of examples. Another is that none of these animals or machines were optimized purely for either of these flight metrics—they all had other constraints or more complex goals. For instance, the paramotor was competing for a record in which a paramotor had to be used, specifically. For the longest human flight, the flying machine had to be capable of carrying a human. The albatross’ body has many functions. Thus it seems plausible that either engineers or natural evolution could reach solutions far better on our metrics than those recorded here if they were directly aiming for those metrics.

The measurements for distance.mass/energy covered a much narrower band than those for distance/energy: a factor of under two orders of magnitude versus around eight. Comparing best scores between evolution and engineering, the gap is also much smaller, as noted above (a factor of less than one order of magnitude versus three orders of magnitude). This seems like some evidence that that band of performance is natural for some reason, and so that more pointed efforts to do better on these metrics would not readily lead to much higher performance.

Primary author: Ronny Fernandez

Notes

Energy efficiency of monarch butterfly flight

Thu, 26 Nov 2020 06:30:47 GMT

Updated Nov 25, 2020

According to very rough estimates, the monarch butterfly:

can fly around 100,000-600,000 m/kJ
and move mass at around 0.065-0.36 kg.m/J

Details

The Monarch Butterfly is a butterfly known for its impressively long migration.¹

Mass

The average mass of a monarch butterfly prior to its annual migration has been estimated to be 600mg²

Distance per Joule

The following table gives some very rough estimates of energy expenditures, speeds and distances for several modes of flight, based on confusing information from a small number of papers (see footnotes for details).

Activity	Description	Energy expenditure per mass ( J/g.h)	Energy expenditure for 600mg butterfly (J/s)	Speed (m/s)	distance/energy (m/J)
Soaring/gliding	Unpowered flight, including gradual decline and ascent via air currents	8-33³	~0.0014 – 0.0056⁴	Very roughly 2.5-3.6 on average⁵	446- 2571⁶
Cruising	Low speed powered flight	Very roughly 209⁷	0.035⁸	Maximum: >5⁹	Maximum: >143¹⁰
Sustained flapping	High speed powered flight	Very roughly 837¹¹	~0.14¹²	Maximum: >13.9¹³	Maximum: >99¹⁴

Table 1: Statistics for several modes of flight. All figures are very rough estimates, based on incomplete and confusing information from a small number of papers (see footnotes for details).

Soaring is estimated to be potentially very energy efficient (see Table 1), since it mostly makes use of air currents for energy. It seems likely that at least a small amount of powered flight is needed for getting into the air, however monarch butterflies can apparently fly for hundreds of kilometers in a day¹⁵, so supposing that they don’t stop many times in a day, taking off seems likely a negligible part of the flight.¹⁶

This would require ideal wind conditions, and our impression is that in practice, butterflies do not often fly very long distances without using at least a small amount of powered flight.¹⁷

There is stronger evidence that monarch butterflies can realistically soar around 85% of the time, from Gibo & Pallett, who report their observations of butterflies under relatively good conditions.¹⁸ So as a high estimate, we use this fraction of the time for soaring, and suppose that the remaining time is the relatively energy-efficient cruising, and take the optimistic end of all ranges. This gives us:

One second of flight = 0.15 seconds cruising + 0.85 seconds soaring

= 0.15s * 5 m/s cruising + 0.85s * 3.6m/s soaring

= 0.75m cruising + 3.06m soaring = 3.81m total

= 0.75m / 143 m/J cruising + 3.06m / 2571 m/J soaring = 0.0064 J total

Thus distance/energy = 3.81m/0.0064 J = 595 m/J

For a low estimate of efficiency, we will assume that all of the powered flight is the most energetic flight, that powered flight is required half the time on average, and that the energy cost of gliding is twice that of resting. This gives us:

Energy efficiency = (50% * soaring distance + 50% * powered distance)/(50% * soaring energy + 50% * powered energy)

= (50% * soaring distance/time + 50% * powered distance/time)/(50% * soaring energy/time + 50% * powered energy/time)

= (0.5 * 2.5m/s + 0.5 * 13.9m/s)/(0.5 * (0.0056 * 2) J/s + 0.5 * 0.14 J/s)

= 108 m/J

Thus we have, very roughly:

distance/energy = 100,000-600,000 m/kJ

Mass.distance per Joule

As noted earlier, the average mass of a monarch butterfly prior to its annual migration has been estimated to be 600mg¹⁹

Thus we have:

mass*distance/energy = 0.0006 kg * 108 — 0.0006 kg * 595 m/J

= 0.065 — 0.36 kg.m/J

Notes

Energy efficiency of wandering albatross flight

Wed, 25 Nov 2020 00:13:13 GMT

Updated Nov 24, 2020

The wandering albatross:

can fly at around 481m/kJ
and moved mass at around 2.8—6.1 kg.m/J

Details

The wandering albatross is a very large seabird that flies long distances on wings with the largest span of any bird.¹

Distance per Joule

Speed

In a study of wandering albatrosses flying in various wind speeds and directions, the fastest ground speed measured appears to be around 24m/s, ²

Energy expenditure

One study produced an estimate that when flying, albatrosses use 2.35 times their basal metabolic rate³ which same paper implies is around 1,833 kJ/bird.day.⁴

That gives us a flight cost for flying of 0.050 kJ/second.⁵

Distance per Joule calculation

This gives us a distance per energy score of:

distance/energy

= 24 m/s / 0.050 kJ/s

= 481m/kJ

Mass.distance per Joule

Albatrosses weigh 5.9 to 12.7 kg.⁶

Thus we can estimate:

mass.distance/Joule

= 5.9kg * 0.48 m/J to 12.7kg*0.48 m/J

= 2.8—6.1kg.m/J

Notes

Energy efficiency of paramotors

Tue, 24 Nov 2020 21:11:22 GMT

Updated Nov 24, 2020

We estimate that a record-breaking two-person paramotor:

covered around = 0.36 m/kJ
and moved mass at around 0.058 – 0.10 kg.m/J

Details

Paramotors are powered parachutes that allow the operator to steer.¹

Distance per Joule

The Fédération Aéronautique Internationale (FAI) maintains records for a number of classes of paramotor contest. We look at subclass RPF2T—(Paramotors : Paraglider Control / Foot-launched / Flown with two persons / Thermal Engine)—which is appears to be the most recent paramotor record for ‘Distance in a straight line with limited fuel’.²

The record distance was 123.18 km.³ The FAI rules state that no more than 7.5 kg of fuel may be used.⁴ We will assume that in the process of breaking this record, all of the available fuel was used. We will also assume that regular gasoline was used. Gasoline has an energy density of 45 MJ/kg.⁵

Distance per energy = 123.18 km / (7.5 kg * 45 MJ/kg)

= 0.36 m/kJ

Mass.distance per Joule

The weight of an entire paramotoring apparatus appears to be the weights of the passengers plus motor plus wing plus clothing and incidentals, based on forum posts.⁶ These posts put clothing and incidentals at around 8kg, but are estimates for single person flying, whereas this record was a two person flight. We guess that two people need around 1.5x as much additional weight, for 12kg.

Wikipedia says that the weight of a paramotor varies from 18kg to 34 kg.⁷ However it is unclear whether this means the motor itself, or all of the equipment involved.

The glider used appears to be MagMax brand, a typical example of which weighs around 8kg, though this may have been different in 2013, or they may have used a different specific glider.⁸ To account for this uncertainty, we shall add the glider weight to the high estimate, and so estimate the weight of the glider and motor together at 18-42kg.

We will assume that the apparently male pilots weighed between 65 and 115 kgs each, based on normal male weights⁹.

Thus we have:

weight = motor + wing + people + clothing and incidentals

weight (low estimate) = 18 + 65*2 + 12 = 160kg

weight (high estimate) = 42 + 115*2 + 12 = 284kg

High efficiency estimate:

284kg * 0.36 m/kJ = 0.10 kg.m/J

Low efficiency estimate:

160kg * 0.36 m/kJ = .058 kg.m/J

This gives us a range of 0.058 – 0.10 kg.m/J

Notes

Misalignment and misuse: whose values are manifest?

Thu, 19 Nov 2020 00:06:34 GMT

By Katja Grace, Nov 18 2020, Crossposted from world spirit sock puppet.

AI related disasters are often categorized as involving misaligned AI, or misuse, or accident. Where:

misuse means the bad outcomes were wanted by the people involved,
misalignment means the bad outcomes were wanted by AI (and not by its human creators), and
accident means that the bad outcomes were not wanted by those in power but happened anyway due to error.

In thinking about specific scenarios, these concepts seem less helpful.

I think a likely scenario leading to bad outcomes is that AI can be made which gives a set of people things they want, at the expense of future or distant resources that the relevant people do not care about or do not own.

For example, consider autonomous business strategizing AI systems that are profitable additions to many companies, but in the long run accrue resources and influence and really just want certain businesses to nominally succeed, resulting in a worthless future. Suppose Bob is considering whether to get a business strategizing AI for his business. It will make the difference between his business thriving and struggling, which will change his life. He suspects that within several hundred years, if this sort of thing continues, the AI systems will control everything. Bob probably doesn’t hesitate, in the way that businesses don’t hesitate to use gas vehicles even if the people involved genuinely think that climate change will be a massive catastrophe in hundreds of years.

When the business strategizing AI systems finally plough all of the resources in the universe into a host of thriving 21st Century businesses, was this misuse or misalignment or accident? The strange new values that were satisfied were those of the AI systems, but the entire outcome only happened because people like Bob chose it knowingly (let’s say). Bob liked it more than the long glorious human future where his business was less good. That sounds like misuse. Yet also in a system of many people, letting this decision fall to Bob may well have been an accident on the part of others, such as the technology’s makers or legislators.

Outcomes are the result of the interplay of choices, driven by different values. Thus it isn’t necessarily sensical to think of them as flowing from one entity’s values or another’s. Here, AI technology created a better option for both Bob and some newly-minted misaligned AI values that it also created—‘Bob has a great business, AI gets the future’—and that option was worse for the rest of the world. They chose it together, and the choice needed both Bob to be a misuser and the AI to be misaligned. But this isn’t a weird corner case, this is a natural way for the future to be destroyed in an economy.

Thanks to Joe Carlsmith for conversation leading to this post.

Energy efficiency of The Spirit of Butt’s Farm

Wed, 18 Nov 2020 23:53:25 GMT

Updated Nov 18, 2020

The Spirit of Butt’s Farm:

covered around 31.67 m/kJ
and moved mass at around 0.16 – 0.086 kg.m/J

Details

The Spirit of Butt’s Farm was a record setting model airplane that crossed the Atlantic on one gallon of fuel.¹ Fully fueled it weighed 4.987 kg, dry it weighed 2.705 kg.²

The record setting flight used 117.1 fluid ounces of fuel.³ The straight line distance of the flight was 3,028.1 km.⁴ It was powered by 88% Coleman lantern fuel, mixed with lubricant.⁵ Coleman fuel is based on naphtha ⁶, so we can use the energy density of naphtha—31.4 MJ/L⁷—as a rough guide to its energy content, though naphtha appears to vary in its content, and it is unclear whether Coleman fuel consists entirely of naphtha.

From all this, we have:

Distance per energy = 3,028.1 km / (117.1 fl oz * 0.88 * 31.4 MJ/L)

= 31.67 m/kJ

For weight times distance per energy we will calculate a best and a worst score. To calculate the best score we will use the fully fueled weight, and to calculate the worst score we will use the dry weight. All other values are the same in both calculations.

Best score:

Distance*mass/energy = 4.987 kg * 31.67 m/kJ

= 0.16 kg.m/J

(4.987 kg * 3,028.1km) / (117.1 US fluid ounces * 31.4MJ/litre) = 0.1389 kg*m/j

Worst score:

Distance*mass/energy = 2.705 kg * 31.67 m/kJ

= .086 kg.m/J

Notes

Photo by Ronan Coyne, licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license, unaltered.

Was the industrial revolution a drastic departure from historic trends?

Tue, 17 Nov 2020 22:14:30 GMT

We do not have a considered view on this topic.

Details

We have not investigated this topic. This is an incomplete list of evidence that we know of:

David Roodman’s analysis of the surprisingness of the industrial revolution under his 2020 model of economic history.¹
Ben Garfinkel’s analysis of whether economic history suggests a singularity²
Robin Hanson’s analysis of historic economic growth understood as a sequence of exponential modes.³
On a log(GWP)-log(doubling time) graph, the industrial revolution appears to be almost perfectly on trend, according to our very crude analysis.

Relevance

The nature of the industrial revolution is relevant to AI forecasting in the following ways:

If growth during the industrial revolution is a highly improbable aberration from longer term trends, it suggests that it is a consequence of specific developments at the time, most saliently new technologies. This suggests that new technologies can sometimes alone cause changes at the level of the global economy.
‘The impact of the industrial revolution’ is sometimes used as a measure against which to compare consequences of AI developments. Thinking here may be sharpened by clarification on the nature of the industrial revolution. This use is likely related to the point above, where ‘the scale of the industrial revolution’ is taken to be a historically plausible scale of impact for the most ambitious technologies.
If economic history is best understood as a sequence of ‘growth modes’, per Hanson 2000,⁴ the industrial revolution being one, this changes our best extrapolation to the future. For instance, we might expect the continuation of the current mode to be slower than in a continuously super-exponential model, but may also expect to meet a new growth mode at some point, which may be substantially faster (and have other characteristics recognizable from past ‘growth mode’ changes). See Hanson 2000 for more on this.

Notes

Energy efficiency of MacCready Gossamer Albatross

Tue, 10 Nov 2020 02:15:26 GMT

Updated Nov 9, 2020

The MacCready Gossamer Albatross:

covered around 2.0—4.6 m/kJ
and moved mass at around 0.1882 —0.4577 kg.m/J

Details

The MacCready Gossamer Albatross was a human-powered flying machine that crossed the English Channel in 1979.¹ The pilot pedaled the craft, seemingly as if on a bicycle. It had a gross mass of 100kg, flying across the channel,² and flew 35.7 km in 2 hours and 49 minutes.³ The crossing was difficult however, so it seems plausible that the Gossamer Albatross could fly more efficiently in better conditions.

We do not know the pilot’s average power output, however:

Wikipedia claims at least 300W was required to fly the craft⁴
Chung 2006, an engineering textbook, claims that the driver, a cyclist, could produce around 200W of power.⁵
Our impression is that 200W is a common power output over houres for amateur cycling. For instance, one of our researchers is able to achieve this for three hours.⁶

The best documented human cycling wattage that we could easily find is from professional rider Giulio Ciccone who won a stage of the Tour de France, then uploaded power data to the fitness tracking site Strava.⁷ His performance suggests around 318W is a reasonable upper bound, supposing that the pilot of the Gossamer Albatross would have had lower performance.⁸

To find the energy used by the cyclist, we divided power output by typical efficiency for a human on a bicycle, which according to Wikipedia ranges from .18 to .26.⁹

Distance per Joule

For distance per energy this gives us a highest measure of:

35.7 km / ((200W * (2 hours + 49 minutes))/0.26) = 4,577 m/MJ

And a lowest measure of:

35.7 km / ((318W * (2 hours + 49 minutes))/0.18) = 1,993 m/MJ

Mass per Joule

For weight times distance per energy this gives us a highest measure of:

(100kg * 35.7 km) / ((200W * (2 hours + 49 minutes))/0.26) = 0.4577 kg*m/j

And a lowest measure of:

(100kg * 35.7 km) / ((318W * (2 hours + 49 minutes))/0.17) = 0.1882 kg*m/j

Notes

Energy efficiency of Boeing 747-400

Fri, 06 Nov 2020 05:10:05 GMT

Updated Nov 5, 2020

The Boeing 747-400:

covers around 0.0021m/kJ.
and moves mass at around 0.39 – 0.83 kg.m/J

Details

The Boeing 747-400 is a 1987 passenger plane.¹

Distance per Joule

The plane uses 10.77 kg/km of fuel on a medium haul flight.² We do not know what type of fuel it uses, but typical values for aviation fuel are around 44MJ/kg.³ Thus to fly a kilometer, the plane needs 10.77 kg of fuel, which is 10.77 x 44 MJ = 474 MJ of fuel. This gives us 0.0021m/kJ.

Mass.distance per Joule

According to Wikipedia, the 747’s ‘operating empty weight’ is 183,523 kg and its ‘maximum take-off weight’ is 396,893 kg.⁴ We use the range 183,523 kg—396,893 kg since we do not know at what weight in that range the relevant speeds were measured.

We have:

Distance per kilojoule: 0.0021m/kJ
Mass: 183,523 kg—396,893 kg

This gives us a range of 0.39 – 0.83 kg.m/J

Notes

Energy efficiency of Airbus A320

Fri, 06 Nov 2020 04:44:03 GMT

Updated Nov 5, 2020

The Airbus A320:

covers around 0.0078 m/kJ
and moves mass at around 0.33 – 0.61 kg.m/J

Details

The Airbus A320 is a 1987 passenger plane.¹

Distance per Joule

The plane uses 2.91 kg of fuel per km on a medium haul flight.² We do not know what type of fuel it uses, but typical values for aviation fuel are around 44MJ/kg.³ Thus to fly a kilometer, the plane needs 2.91kg of fuel, which is 2.91 x 44 MJ = 128MJ of fuel. This gives us 0.0078 m/kJ

Mass.distance per Joule

According to modernairliners.com, the A320’s ‘operating empty weight’ is 42,600 kg and its ‘maximum take-off weight’ is 78,000 kg.⁴ We use the range 42,600—78,000 kg, since we do not know at what weight in that range the relevant speeds were measured.

We have:

Distance per kilojoule: 0.0078 m/kJ
Mass: 42,600—78,000 kg

This gives us a range of 0.33 – 0.61 kg.m/J

Notes

Energy efficiency of North American P-51 Mustang

Fri, 06 Nov 2020 01:04:24 GMT

Updated Nov 5, 2020

The North American P-51 Mustang:

flew around 0.073—0.092 m/kJ
and moved mass at around 0.25 – 0.50 kg.m/J

Details

The North American P-51 Mustang was a 1940 US WWII fighter and fighter-bomber.¹

Mass

According to Wikipedia²:

Empty weight: 7,635 lb (3,465 kg)
Gross weight: 9,200 lb (4,175 kg)
Max takeoff weight: 12,100 lb (5,488 kg)

We use the range 3,465—5,488 kg, since we do not know at what weight in that range the relevant speeds were measured.

Distance per Joule

Wikipedia tells us that cruising speed was 362 mph (162 m/s)³

A table from WWII Aircraft Performance gives combinations of flight parameters apparently for a version of the P-51, however it has no title or description, so we cannot be confident. ⁴ We extracted some data from it here. This data suggests the best combination of parameters gives a fuel economy of 6.7 miles/gallon (10.8km)

We don’t know what fuel was used, but fuel energy density seems likely to be between 31—39 MJ/L = 117—148 MJ/gallon.⁵

Thus the plane flew about 10.8km on 117—148 MJ of fuel, for 0.073—0.092 m/kJ

Mass.distance per Joule

We have:

Distance per kilojoule: 0.073—0.092 m/kJ
Mass: 3,465—5,488 kg

This gives us a range of 0.25 – 0.50 kg.m/J

Notes

Energy efficiency of Vickers Vimy plane

Thu, 05 Nov 2020 20:54:31 GMT

Updated Nov 5, 2020

The Vickers Vimy:

flew around 0.025—0.050 m/kJ
and moved mass at around 0.081 – 0.25 kg.m/J

Details

The Vickers Vimy was a 1917 British WWI bomber.¹ It was used in the first non-stop transatlantic flight.

Mass

According to Wikipedia²:

Empty weight: 7,104 lb (3,222 kg)
Max takeoff weight: 10,884 lb (4,937 kg)

We use the range 3,222—4,937 kg, since we do not know at what weight in that range the relevant speeds were measured.

Energy use per second

We also have:

Power: 360 horsepower = 270 kW³
Efficiency of use of energy from fuel: we did not find data on this, so use an estimate of 15%-30%, based on what we know about the energy efficiency of the Wright Flyer.

From these we can calculate:

Energy use per second
= power of engine x 1/efficiency in converting energy to engine power
= 270kJ/s / .15—270kJ/s / .30
= 900—1800 kJ/s

Distance per second

Wikipedia gives us:

Maximum speed: 100 mph (160 km/h, 87 kn)⁴

Note that the figures for power do not obviously correspond to the highest measured speed. This is a rough estimate.

Distance per Joule

We now have (from above):

speed = 100 miles/h = 44.7m/s
energy use = 900—1800 kJ/s

Thus, on average each second the plane flies 44.7 m and uses 900—1800 kJ, for 0.025—0.050 m/kJ.

Mass.distance per Joule

We have:

Distance per kilojoule: 0.025—0.050 m/kJ
Mass: 3,222—4,937 kg

This gives us a range of 0.081 – 0.25 kg.m/J

Notes

Energy efficiency of Wright model B

Thu, 05 Nov 2020 20:09:52 GMT

Updated Nov 5, 2020

The Wright model B:

flew around 0.10—0.21m/kJ
and moved mass at around 0.036 – 0.12 kg.m/J

Details

The Wright Model B was a 1910 plane developed by the Wright Brothers.¹

Mass

According to Wikipedia²:

Empty weight: 800 lb (363 kg)
Gross weight: 1,250 lb (567 kg)

We use the range 363—567 kg, since we do not know at what weight in that range the relevant speeds were measured.

Energy use per second

From Wikipedia, we have³:

Power: 35 horsepower = 26kW
Efficiency of use of energy from fuel: we could not find data on this, so use an estimate of 15%-30%, based on what we know about the energy efficiency of the Wright Flyer.

From these we can calculate:

Energy use per second
= power of engine x 1/efficiency in converting energy to engine power
= 26kJ/s / .15—26kJ/s / .30
= 86.6—173 kJ/s

Distance per second

Wikipedia gives us⁴:

Maximum speed: 45 mph (72 km/h, 39 kn)
Cruise speed: 40 mph (64 km/h, 35 kn)

We use the cruise speed, as it seems more likely to represent speed achieved with the energy usages reported.

Distance per Joule

We now have (from above):

speed = 40miles/h = 17.9m/s
energy use = 86.6—173 kJ/s

Thus, on average each second the plane flies 17.9m and uses 86.6—173 kJ, for 0.10—0.21m/kJ.

Mass.distance per Joule

We have:

Distance per kilojoule: 0.10—0.21m/kJ
Mass: 363—567kg

This gives us a range of 0.036 – 0.12 kg.m/J

Notes

Energy efficiency of Wright Flyer

Wed, 04 Nov 2020 18:58:55 GMT

The Wright Flyer:

flew around 0.08-0.17m/kJ
and moved mass at around .02 – .06 kg.m/J

Details

The Wright Flyer (Flyer I) was the first successful plane, built in 1903.¹

Mass

According to Wikipedia²:

Empty weight: 605 lb (274 kg)
Max takeoff weight: 745 lb (338 kg)

Energy use per second

Fuel use per hour

A 1904 article in the Minneapolis Journal says the plane consumed ‘a little less than than ten pounds of gasoline per hour’.³ A pound of gasoline contains around 20MJ⁴ So we have:

Hourly fuel consumption: 10lb/h x 20MJ/lb = 200MJ/h = 55kJ/s

We don’t know how reliable this source is. For instance, a 1971 book, The Write Brothers’ Engines and their Design does not give data on fuel consumption in their table of engine characteristics for lack of available comprehensive data⁵, suggesting that its authors did not consider the article strong evidence, though it is also possible that they didn’t have access to the 1904 article.

Utilized motor power / efficiency

To confirm, we can estimate the plane’s energy use per second by a second means: combining the claimed power (energy/second) made use of by the engine, and a guess about how much fuel is needed to deliver that amount of energy.

Utilized motor power

According to Wikipedia, the plane had a 12 horsepower (9 kJ/s), gasoline engine.⁶ The 1904 Minneapolis Journal article put it at 16 horsepower (12 kJ/s), and Orville Wright, quoted by Hobbs (1971), puts it at ‘almost 16 horsepower’ at one point.⁷ Hobbs says that at one point this engine achieved 25 horsepower, though this probably isn’t representative of what was ‘actually utilized’.⁸ For that he gives a range of 8.25-16 horsepower.⁹ In light of these estimates, we will use 8.25-16 horsepower, which is 6.15-12 kJ/s.

Efficiency of energy conversion from fuel to motor power

A quote from Orville Wright suggests fuel consumption as 0.580lb of fuel per horsepower hour.¹⁰ This would imply 23% of energy was utilized from the fuel.¹¹

Hobbs notes that this seems low, but assumes a similar efficiency: 24.50%. Thus he presumably doesn’t find it implausible.¹²

According to Wikipedia, the thermal efficiency of a typical gasoline engine is 20%¹³. It seems that this has increased¹⁴, which would suggest that the typical figure was lower in 1903. We don’t think this undermines the more specific figures given above.

It seems likely that Hobbs number is best here, since he knows about Wright’s number, and may have more information than us about for instance the exact fuel being used. So we use 24.5%.

Calculation of energy use from motor power/efficiency

Combining these numbers, we have:

power spent = 6.15-12 kJ/s used by engine x 1 / 24.5% fuel energy needed to get one unit of energy used by engine, given inefficiency

= 25-49 kJ/s

Calculation of energy use

We now have:

energy expenditure calculated via motor power and efficiency: 25-49 kJ/s
energy expenditure calculated via hourly fuel use: 55kJ/s

Neither figure seems clearly more reliable, so we will use the range 25-55kJ/s

Distance per second

Two of the Wright Flyer’s first flights were 120 feet in 12 seconds and 852 feet in 59 seconds.¹⁵ This gives speeds of 3m/s and 4.4m/s. We will use the second, since it better represents successful flight, still within the first days.

Distance per Joule

We now have (from above):

speed = 4.4 m/s
energy use = 25-55 kJ/s

Thus, on average each second the plane flies 4.4m and uses 25-55kJ, for 0.08-0.17m/kJ.

Mass.distance per Joule

We have:

Distance per kilojoule: 0.08-0.17m/kJ
Mass: 274-338kg

This gives us a range of .02 – .06 kg.m/J

Automated intelligence is not AI

Sun, 01 Nov 2020 23:38:44 GMT

By Katja Grace, Nov 1 2020, Crossposted from world spirit sock puppet.

Sometimes we think of ‘artificial intelligence’ as whatever technology ultimately automates human cognitive labor.

I question this equivalence, looking at past automation. In practice human cognitive labor is replaced by things that don’t seem at all cognitive, or like what we otherwise mean by AI.

Some examples:

Early in the existence of bread, it might have been toasted by someone holding it close to a fire and repeatedly observing it and recognizing its level of doneness and adjusting. Now we have machines that hold the bread exactly the right distance away from a predictable heat source for a perfect amount of time. You could say that the shape of the object embodies a lot of intelligence, or that intelligence went into creating this ideal but non-intelligent tool.
Self-cleaning ovens replace humans cleaning ovens. Humans clean ovens with a lot of thought—looking at and identifying different materials and forming and following plans to remove some of them. Ovens clean themselves by getting very hot.
Carving a rabbit out of chocolate takes knowledge of a rabbit’s details, along with knowledge of how to move your hands to translate such details into chocolate with a knife. A rabbit mold automates this work, and while this route may still involve intelligence in the melting and pouring of the chocolate, all rabbit knowledge is now implicit in the shape of the tool, though I think nobody would call a rabbit-shaped tin ‘artificial intelligence’.
Human pouring of orange juice into glasses involves various mental skills. For instance, classifying orange juice and glasses and judging how they relate to one another in space, and moving them while keeping an eye on this. Automatic orange juice pouring involves for instance a button that can only be pressed with a glass when the glass is in a narrow range of locations, which opens an orange juice faucet running into a spot common to all the possible glass-locations.

Some of this is that humans use intelligence where they can use some other resource, because it is cheap on the margin where the other resource is expensive. For instance, to get toast, you could just leave a lot of bread at different distances then eat the one that is good. That is bread-expensive and human-intelligence-cheap (once you come up with the plan at least). But humans had lots of intelligence and not much bread. And if later we automate a task like this, before we have computers that can act very similarly to brains, then the alternate procedure will tend to be one that replaces human thought with something that actually is cheap at the time, such as metal.

I think a lot of this is that to deal with a given problem you can either use flexible intelligence in the moment, or you can have an inflexible system that happens to be just what you need. Often you will start out using the flexible intelligence, because being flexible it is useful for lots of things, so you have some sitting around for everything, whereas you don’t have an inflexible system that happens to be just what you need. But if a problem seems to be happening a lot, it can become worth investing the up-front cost of getting the ideal tool, to free up your flexible intelligence again.

Time for AI to cross the human range in English draughts

Mon, 26 Oct 2020 22:28:36 GMT

Updated 26 Oct 2020

AI progress in English draughts performance crossed the following ranges in the following times:

Range	Start	End	Duration (years)
First attempt to beginner level	1951	~1956, <1961	~4, <10
Beginner to superhuman	~1956, <1961	1994	~38, >33
Above superhuman	1994	2007*	13*

* treating perfect play as the end of progress, though progress could potentially be made in performing better against imperfect play.

Details

Metric

‘English Draughts’ is a popular variety of draughts, or checkers.

Here we look at direct success of AI in beating human players, rather than measuring humans and AI on a separate metric of strength.

Data

Data here comes mostly from Wikipedia. We found several discrepancies in Wikipedia’s accounts of these events, so consider the remaining data to be somewhat unreliable.

AI achievement of human milestones

Earliest attempt

According to Wikipedia, the first checkers program was run in 1951.¹

Beginner level

There seems to be some ambiguity around the timing and performance of Arthur Samuel’s early draughts programs, but it appears that he worked on them from around 1952. In 1956, he demonstrated a program on television. ² It is unclear how good the program’s play was, but it is said to have resulted in a fifteen-point rise in the stock price of IBM when demonstrated to IBM shareholders, seemingly at around that time. This weakly suggests that the program played at at least beginner level.

In 1962 Samuel’s program apparently beat an ambiguously skilled player who would by four-years later become a state champion in Connecticut.³ Thus progress was definitely beyond beginner level by 1962.

Superhuman level

In 1994, computer program Chinook drew six times against world champion Marius Tinsley, before Tinsley withdrew due to pancreatic cancer and Chinook officially won.⁴ Thus Chinook appears to have been close to as good as the best player in 1994.

End of progress

In 2007 checkers was ‘weakly solved’, which is to say that perfect play guaranteeing a draw for both sides from the start of the game is known, from the starting state⁵ (this does not imply that if someone plays imperfectly, perfect moves following this are known).⁶ This is not the best possible performance by all measures, since further progress could presumably be made on reliably beating worse players.

Times for AI to cross human-relative ranges

Given the above dates, we have:

Range	Start	End	Duration (years)
First attempt to beginner level	1951	~1956, <1961	~4, <10
Beginner to superhuman	~1956, <1961	1994	~38, >33
Above superhuman	1994	2007*	13*

* treating perfect play as the end of progress, though progress could potentially be made in performing better against imperfect play.

Notes

Time for AI to cross the human range in StarCraft

Tue, 20 Oct 2020 20:55:24 GMT

Updated 22 Oct 2020

Progress in AI StarCraft performance took:

~0 years to reach the level of an untrained human
~21 years to pass from untrained human level to high professional human level
~2 years to continue from trained human to current performance (2020), with no particular end in sight.

Details

Metric

We compare human and AI players on their direct ability to beat one another (rather than a measure of the overall performance of each).

AI milestones

Earliest attempt

Starcraft was released in 1998¹ The game allows the player to play against a computer opponent, however this built-in AI has access to information that a normal player would not have. For instance, it has real-time information about what the other player is doing at all times, which is normally hidden. We do not have detailed knowledge about early StarCraft AIs that do not have this advantage, but our impression is that it was possible to write them from the start (see next section).

Beginner level

Our impression is that since StarCraft Brood War came out in 1998, it has been possible to write a bot that can beat a player who recently learned the game, without “cheating” in the way that the game’s built-in computer opponents do. This is uncertain, and based on private discussion with people who write Starcraft AIs that compete in tournaments.

Professional level

In 2018, DeepMind’s AlphaStar beat MaNa², a strong professional player (seemingly 13th place in the 2018 StarCraft II World Championship Series Circuit)³. This does not imply that AlphaStar was in general a stronger player than MaNa, but suggests AlphaStar was at a broadly high professional level. How AlphaStar’s performance compares to humans will depend on how narrowly the task is defined, so that if the AI is not allowed to give commands faster than a human is able, it will compare less favorably than if it is allowed to give commands very quickly⁴.

Times for AI to cross human-relative ranges

Given the above dates, we have:

Range	Start	End	Duration (years)
First attempt to beginner level	1998	1998	~0
Beginner to superhuman	1998	2018	~21
Above superhuman	2018	?	>2

Notes

Time for AI to cross the human performance range in ImageNet image classification

Mon, 19 Oct 2020 23:52:50 GMT