AI and why we should all be worried - AI's dirty secrets

I gave a talk on AI for the Swindon Science Cafe. Here below are the slides, followed by my notes.

Slide 1

Hi, my name is Alan Winfield. Thank you Rod and Claire for inviting me to speak this evening.

Slide 2

So, what does a robot and AI ethicist do? Well, the short answer is worry.

I worry about the ethical and societal impact of AI on individuals, society and the environment. Here are some keywords on ethics, reflecting that we must work toward AI that respects Human Rights, diversity and dignity, is unbiased and sustainable, transparent, accountable and socially responsible. I also work in Standards with both the British Standards Institute and the International IEEE Standards Association.

Slide 3

Before getting into the ethics of AI I need to give you a quick tutorial on machine learning.

The most powerful and exciting AI today is based on Artificial Neural Networks (ANNs). Here is a simplified diagram of a Deep Learning network for recognizing images. Each small circle is a *very* simplified mathematical model of biological neurons, and the outputs of each layer of artificial neurons feed the inputs of the next layer. In order to be able to recognise images the network must first be trained with images that are already labelled - in this case my dear late dog Lola.

But in order to reliably recognise Lola the network needs to be trained not with one picture of Lola but many. This set of images is called the training data set and without a good data set the network will not work at all or will be biased. (In reality there will need to be not 4 but hundreds of images of Lola).

But even a simple ANN can get things wrong. A famous example was an ANN like this trained on pictures of wolves. After training they input of a bear, but the network identified it as a wolf. Why? Because all of the wolf pictures had snowy backgrounds, so the network learned to recognize snow, not a wolf. The bear also had a snowy background.

This is one example of what we now call a ‘hallucination’ in big AIs.

Slide 4

We’ve been worrying about the existential threat of AI for a long time: here is a piece I wrote for the Guardian in 2014, when ‘the singularity was the main thing we worried about.

The singularity is the idea that as soon as AI is smarter than humans then the AIs will rapidly improve themselves, with unforeseeable consequences for human civilization.

But the singularity is a thing for the techno-utopians: wealthy middle-aged men who regard the singularity as their best chance of immortality. They are Singularitarians, some of whom appear prepared to go to extremes to stay alive for long enough to benefit from a benevolent super-AI - a manmade god that grants transcendence.

And it's a Thing for the doomsayers, the techno-dystopians. Apocalypsarians who are equally convinced that a superintelligent AI will have no interest in curing cancer or old age, or ending poverty, but will instead - malevolently or maybe just accidentally - bring about the end of human civilisation as we know it. History and Hollywood are on their side. From the Golem to Frankenstein's monster, Skynet and the Matrix, we are fascinated by the old story: man plays god and then things go horribly wrong.

Slide 5

Today we have influential scientists who worry about Artificial General Intelligence (AGI). Notable among these is physicist and cosmologist Max Tegmark.

At the AI safety summit in February 2025 Tegmark argued that we need a middle pathway between No AI and Uncontrollable AGI, which he calls guaranteed safe tool AI. He suggested a policy solution in which the US and China both launch national safety standards preventing their own AI companies from building AGI. Leading to what Tegmark rather optimistically calls ‘an age of unprecendented global prosperity powered by safe tool AI.

Is there an existential threat from AI itself? No. I fear human stupidity much more than artificial intelligence.

So should we be worried? Yes. But the things I worry about are rather more down to earth.

In the rest of this talk I will consider the energy costs of AI, then the human costs.

Slide 6

In 2016 Go champion Lee Sedol was famously defeated by DeepMind's AlphaGo. It was a remarkable achievement for AI. But consider the energy cost. In a single two-hour match Sedol burned around 170 kcals: roughly the amount of energy you would get from an egg sandwich. Or about 1 Watt – the power of an LED night light.

In the same two hours the AlphaGo machine reportedly consumed about 50,000 Watts. The same as a 50 kW generator for industrial lighting. And that's not taking account of the energy used to train AlphaGo.

Slide 7

A paper published in 2019 paper revealed, for the first time, estimates of the carbon cost of training large AI models for natural language processing such as machine translation. The carbon cost of simple models is quite modest, but with tuning and experimentation the carbon cost leaps to 7 times the carbon footprint of an average human in one year (or 2 times if you're an American).

And the energy cost of optimizing the biggest model is a staggering 5 times the carbon cost of a car over its whole lifetime, including manufacturing it in the first place. The dollar cost of that amount of energy is estimated at between one and 3 million US$. (Something that only companies with very deep pockets can afford.)

These energy costs seem completely at odds with the urgent need to meet sustainable development goals. At the very least AI companies need to be honest about the huge energy costs of machine learning.

Slide 8

At the same Paris meeting earlier this year AI ethicist Kate Crawford drew attention to both energy and water costs of AI. Crawford predicts that the energy cost of training generative AIs will soon overtake the total energy consumption of industrialized nations such as Japan.

She also drew attention to the colossal amounts of clean water that AI server farms need to keep them cool. Water that is already a scarce resource.

One study estimated that ChatGPT-3 requires 700,000 litres of clean water for training, And that each user interaction costs around half a litre of water.

Source: https://interestingengineering.com/innovation/training-chatgpt-consumes-water

Slide 9

The very same Kate Crawford, together with a colleague, produced this extraordinary map of the entire process behind the Amazon Echo.

The remarkable Anatomy of an AI System, shows The Amazon Echo as an anatomical map of human labour, data and planetary resources. 

The map is far too detailed to see on this slide but let me just zoom in on the top of this very large iceberg where we find the amazon echo and its human user, shown here in a yellow dashed box.

Slide 10

At the very top of this pyramid of materials and energy (on the left) and waste (on the right) is you – the user of the Amazon Echo – and your unpaid human labour providing habits and preferences that will be used as training data. 

I strongly recommend you check this out. It is truly eye opening.

Slide 11

Now I want to turn to the human cost of AI.

It is often said that one of the biggest fears around AI is the loss of jobs. In fact the opposite is happening. Many new jobs are being created, but the tragedy is that they are not great jobs, to say the least. Let me introduce you to 3 of these new kinds of jobs.

Conversational AI or chat bots also need human help. Amazon for instance employs thousands of both full-time employees and contract workers to listen to and annotate speech. The tagged speech is then fed back to Alexa to improve its comprehension. In 2019 the Guardian reported that Google employs around 100,000 temps, vendors and contractors: literally an army of linguists to create the handcrafted data sets required for Google translate to learn dozens of languages. Not surprisingly there is a considerable disparity between the wages and working conditions of these workers and Google's full-time employees.

AI tagging jobs are dull, repetitive and in the case of the linguists highly skilled.

Slide 12

Consider AI tagging of images. This is the manual labelling of objects in images to, for instance, generate training data sets for driverless car AIs. Better (and safer) AI needs huge training data sets and a whole new outsourced industry has sprung up all over the world to meet this need. Here is an AI tagging factory in China.

Slide 13

But by far the worst kind of new white-collar job in the AI industry is content moderation.

These tens of thousands of people, employed by third-party contractors, are required to watch and vet offensive content: hate speech, violent pornography, cruelty and sometimes murder of both animals and humans for Facebook, YouTube and other media platforms. These jobs are not just dull and repetitive they are positively dangerous. Harrowing reports tell of PTSD-like trauma symptoms, panic attacks and burn out after one year, alongside micromanagement, poor working conditions and ineffective counselling. And very poor pay - typically $28,800 a year. Compare this with average annual salaries at Facebook of $250,000+.

Slide 14

The extent to which AI has a human supply chain was a big revelation, and I am an AI insider! The genius designers of this amazing tech rely on both huge amounts of energy and a hidden army of what Mary Gray and Siddhartha Suri call Ghost Workers.

I would ask you to consider the question: how can we, as ethical consumers, justify continuing to make use of unsustainable and unethical AI technologies?

Slide 15  

AI ethics are important because AIs are already causing harm. Actually a *very* wide range of harms.

Fortunately, there is an excellent crowdsourced database which collects reports of accidents and near misses involving robots (including autonomous vehicles) and AIs.

The AI incidents database covers both accidents and near misses. I strongly recommend you check it out.

It is important to note that because the AI incidents database is crowdsourced from accidents and near misses that made it into the press and media, what we see is almost certainly only the tip of the iceberg of the harms being done by AI.

The database contains some truly shocking cases. One is a 14-year-old boy, who died by suicide after reportedly becoming dependent on Character.ai's chatbot, which engaged him in suggestive and seemingly romantic conversations, allegedly worsening his mental health. Source: Can A.I. Be Blamed for a Teen’s Suicide? New York Times, Oct 2024.

The database also highlights the criminal use of AI. Examples include criminals phoning parents claiming they have kidnapped a child and demanding a ransom, with deepfake audio of the teenage girl audible in the background. And there are many examples of sextortion, using deepfake AI generated video of famous individuals engaged in sex acts.

The database really highlights the sad truth that AI is a gift to criminals.

Slide 16  

Another more recent database tracks the misuse of AI by lawyers when preparing court cases. The database only shows those instances where the judge (or another officer in the court) spotted the hallucinated decisions, citations or quotations. The cases were thrown out, and in some cases the lawyers found to be using Ai were fined or reported to their bar associations.

While this is not criminal misuse of AI, it does demonstrate a lack of understanding of AI, or at best, naivety. Perhaps the real culprits are hard pressed paralegals. This database underlines the need for professionals to be property trained in AI and its weaknesses.

Since I grabbed this screen shot the number if cases reported has grown.

Slide 17

Lawyer Graziano Mioli elegantly argues that we have a categorical imperative to be imperative when interacting with AIs. Noting that this is not an invitation to be rude.

For Graziano's slides see https://www.youtube.com/watch?v=tjBnGN4u1GA&ab_channel=GrazianoMioli  

I can see why a majority of people interact kindly with AIs. I think it reflects well on those who do say ‘please’, for the Kantian reason that we should not get into the habit of acting unkindly. (Kant famously argued that you should not kick a dog - not for animal welfare reasons, but because you might then be more inclined to kick a human).

See https://www.theaihunter.com/news/ai-etiquette-why-some-people-say-please-to-chatbots/

Slide 18

Having mostly elaborated on the dangers of AI, I want to finish on a positive.

We already enjoy the benefit of useful and reliable AI technology, like smart maps and machine translation. DeepMind's diagnostic AI can detect over 50 eye diseases from retinal scans as accurately as a doctor, and DeepScribe provides automated note taking during a consultation, linking with patient electronic health records

Thank you! 

 

This talk is an extended and updated version of a short talk I gave in June 2019.

The Energy Cost of Online Living in Lockdown

Readers of his blog will know that one of the many things ethical I worry about is the energy cost of AI. As part of the work I'm doing with Claudia Pagliari and her National Expert Group on Digital Ethics for Scotland I've been looking also into the energy costs of what is - for many of us - everyday digital life in lockdown. I don't yet have a complete set of results but what I have found so far is surprising - and not in a good way.

So far I've looked into the energy costs of (i) uploading to the cloud, (ii) streaming video (i.e. from iPlayer or Netflix), and (iii) video conferencing.

(i) Uploading to the cloud. This 2017 article in the Stanford Magazine explains that when you save a 1 Gbyte file – that’s about 1 hour of video - to your laptop’s disk drive the energy cost is 0.000005 kWh, or 5 milliWatt hours. Save the same file to the Cloud and the energy cost is between 3 and 7 kWh. For comparison your electric kettle burns about 3 kWh. This mean that the energy cost of saving to the cloud is about a million times higher than to your local disk drive. 

The huge difference makes sense when you consider that there is a very complex international network of switches, routers and exchange hubs, plus countless amplifiers maintaining signal strength over long distance transmission lines. All of this consumes energy. Then add a slice of the energy costs of the server farm.

(ii) Streaming video. This article in The Times from May 2019 makes the claim that streaming a 2 hour HD movie from Netflix incurs the same energy cost as boiling 10 kettles (based on the sustainable computing research of Mike Hazas). To estimate  how much energy that equates to we need to guess how full the kettle is. A half full 3kWh kettle will take about 2 minutes to boil, and consume therefore 100 Watts. Do that 10 times and you've burned 1kW. A DVD player typically consumes 8 Watts, so streaming costs 125 times more energy.

Again this makes sense against uploading to the cloud, except that here you are downloading from Netflix servers. A 2 hour HD movie is alot of data, around 10GBytes, so 10 times more than the case for (i) above.

(iii) Video conferencing. This post on David Mytton's excellent blog explores the energy cost of Zoom meetings in some detail. David estimates that a 1 hour video zoom call with 6 participants generates between 5 and 15GB of data and that the data transfer consumes between 0.07 – 0.22kWh of electricity. Using our benchmark of kettles boiled this is pretty modest - at most less than one tenth of the energy cost. 

However this estimate makes 2 assumptions: first that you are connected via cable or fixed line - which here in the UK costs 0.015kWh per GByte. A mobile connection costs about seven times that at 0.1kWh/GB. And second, this estimate measures only the energy costs of data transmission and fails to take account of the energy costs of Zoom's data centres, which - if (i) and (ii) here are anything to go by, could be significant, especially since there aren't any in the UK and the default servers are in the US.

As this article on the Zoom blog explains, Zoom calls are not peer to peer. The video from each participant is streamed first to a zoom server then broadcast to every other person on the call. As David Mytton says Zoom don't release information on the overall energy costs of calls. I strongly suspect that if server energy costs were factored in they would be in line with cases (i) and (ii) above. Even so, I feel sure that David Mytton's overall conclusion remains true: that the energy cost of Zoom meetings is significantly lower than all but local or regional travel.

 

I would like to see networking services like cloud storage, video on demand and video conferencing publish a meaningful energy cost. When we buy packaged food from the supermarket we expect to read the calorific energy value of each item, broken down into fat, salt and so on.  It would be great if every online transaction, from sending an email, to watching a movie revealed its energy/carbon cost. Not just for energy geeks like me, but to remind all of us that the Digital Economy is *very* energy hungry.

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I would welcome any additional data which either adds to the above (especially the energy costs for smaller online transactions like tweets, emails or card payments), or shows that the estimates above are wrong. 

Related blog posts:

On Sustainable Robotics
Energy and Exploitation: AIs dirty secrets
What's wrong with Consumer Electronics? 

 

On the simulation (and energy costs) of human intelligence, the singularity and simulationism

For many researchers the Holy Grail of robotics and AI is the creation of artificial persons: artefacts with equivalent general competencies as humans. Such artefacts would literally be simulations of humans. Some researchers are motivated by the utility of AGI; others have an almost religious faith in the transhumanist promise of the technological singularity. Others, like myself, are driven only by scientific curiosity. Simulations of intelligence provide us with working models of (elements of) natural intelligence. As Richard Feynman famously said ‘What I cannot create, I do not understand’. Used in this way simulations are like microscopes for the study of intelligence; they are scientific instruments.

Like all scientific instruments simulation needs to be used with great care; simulations need to be calibrated, validated and – most importantly – their limitations understood. Without that understanding any claims to new insights into the nature of intelligence – or for the quality and fidelity of an artificial intelligence as a model of some aspect of natural intelligence – should be regarded with suspicion.

In this essay I have critically reflected on some of the predictions for human-equivalent AI (AGI); the paths to AGI (and especially via artificial evolution); the technological singularity, and the idea that we are ourselves simulations in a simulated universe (simulationism). The quest for human-equivalent AI clearly faces many challenges. One (perhaps stating the obvious) is that it is a very hard problem. Another, as I have argued in this essay, is that the energy costs are likely to limit progress.

However, I believe that the task is made even more difficult for two further reasons. The first is – as hinted above – that we have failed to recognize simulations of intelligence (which all AIs and robots are) as scientific instruments, which need to be designed, operated and results interpreted, with no less care than we would a particle collider or the Hubble telescope.

The second, and more general observation, is that we lack a general (mathematical) theory of intelligence. This lack of theory means that a significant proportion of AI research is not hypothesis  driven, but incrementalist and ad-hoc. Of course such an approach can and is leading to interesting  and (commercially) valuable advances in narrow AI. But without strong theoretical foundations, the grand challenge of human-equivalent AI seems rather like trying to build particle accelerators to understand the nature of matter, without the Standard Model of particle physics.

The text above is the concluding discussion of my essay On the simulation (and energy costs) of human intelligence, the singularity and simulationism, which appears in an edited collection of essays in a book called From Astrophysics to Unconventional Computation. Published in April 2019, the book marks the 60th birthday of astrophysicist, computer scientist and all round genius, Susan Stepney.

Note: regular visitors to the blog will recognise themes covered in several previous blog posts, brought together in I hope a coherent and interesting way.

Energy and Exploitation: AIs dirty secrets

A couple of days ago I gave a short 15 minute talk at an excellent 5x15 event in Bristol. The talk I actually gave was different to the one I'd originally suggested. Two things prompted the switch: one was seeing the amazing line up of speakers on the programme - all covering more or less controversial topics - and the other was my increasing anger in recent months over the energy and human costs of AI. So it was that I wrote a completely new talk the day before this event.

But before I get to my talk I must mention the amazing other speakers: we heard Phillipa Perry speaking on child parent relationships, Hallie Rubenhold on the truth about Jack the Ripper's victims,  Jenny Riley speaking very movingly about One25's support for Bristol's (often homeless) sex workers, and Amy Sinclair introducing her activism with Extinction Rebellion.

Here is the script for my talk (for the slides go to the end of this blog post).

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Artificial Intelligence and Machine Learning are often presented as bright clean new technologies with the potential to solve many of humanity's most pressing problems.

We already enjoy the benefit of truly remarkable AI technology, like machine translation and smart maps. Driverless cars might help us get around before too long, and DeepMind's diagnostic AI can detect eye diseases from retinal scans as accurately as a doctor.

Before getting into the ethics of AI I need to give you a quick tutorial on machine learning. The most powerful and exciting AI today is based on Artificial Neural Networks. Here [slide 3] is a simplified diagram of a Deep Learning network for recognizing images. Each small circle is a *very* simplified mathematical model of biological neurons, and the outputs of each layer of artificial neurons feed the inputs of the next layer. In order to be able to recognise images the network must first be trained with images that are already labelled - in this case my dog Lola.

But in order to reliably recognise Lola the network needs to be trained not with one picture of Lola but many. This set of images is called the training data set and without a good data set the network will not work at all or will be biased. (In reality there will need to be not 4 but hundreds of images of Lola).

So what does an AI ethicist do? Well, the short answer is worry. I worry about the ethical and societal impact of AI on individuals, society and the environment. Here are some keywords on ethics [slide 4], reflecting that we must work toward AI that respects Human Rights, diversity and dignity, is unbiased and sustainable, transparent, accountable and socially responsible.

But I do more than just worry. I also take practical steps like drafting ethical principles, and helping to write ethical standards for the British Standards Institute and the IEEE Standards Association. I lead P7001: a new standard on transparency in of autonomous systems based on the simple ethical principle that it should always be possible to find out why an AI made a particular decision. I have given evidence in parliament several times, and recently took part in a study of AI and robotics in healthcare and what this means for the workforce of the NHS.

Now I want to share two serious new worries with you.

The first is about the energy cost of AI. In 2016 Go champion Lee Sedol was famously defeated by DeepMind's AlphaGo. It was a remarkable achievement for AI. But consider the energy cost. In a single two hour match Sedol burned around 170 kcals: roughly the amount of energy you would get from an egg sandwich.  Or about the power of an LED night light -  1 Watt. In the same two hours the AlphaGo machine reportedly consumed 50,000 times more energy than Sedol. Equivalent to a 50 kW generator for industrial lighting. And that's not taking account of the energy used to train AlphaGo.

Now some people think we can make human equivalent AI by simulating the human brain. But the most complex animal brain so far simulated is that of c-elegans – the nematode worm. It has 302 neurons and about 5000 synapses - these are the connections between neurons. A couple of years ago I worked out that simulating a neural network for a simple robot with only a 10th the number of neurons of c-elegans costs 2000 times more energy than the whole worm.

In a new paper that came out just a few days ago we have for the first time estimates of the carbon cost of training large AI models for natural language processing such as machine translation [1]. The carbon cost of simple models is quite modest, but with tuning and experimentation the carbon cost leaps to 7 times the carbon footprint of an average human in one year (or 2 times if you're an American).

And the energy cost of optimising the biggest model is a staggering 5 times the carbon cost of a car over its whole lifetime, including manufacturing it in the first place. The dollar cost of that amount of energy is estimated at between one and 3 million US$. (Something that only companies with very deep pockets can afford.)

These energy costs seem completely at odds with the urgent need to halve carbon dioxide emissions by 2030. At the very least AI companies need to be honest about the huge energy costs of machine learning.

Now I want to turn to the human cost of AI. It is often said that one of the biggest fears around AI is the loss of jobs. In fact the opposite is happening. Many new jobs are being created, but the tragedy is that they are not great jobs, to say the least. Let me introduce you to two of these new kinds of jobs.

The first is AI tagging. This is manually labelling objects in images to, for instance, generate training data sets for driverless car AIs. Better (and safer) AI needs huge training data sets and a whole new outsourced industry has sprung up all over the world to meet this need. Here [slide 9] is an AI tagging factory in China.

Conversational AI or chat bots also need human help. Amazon for instance employs thousands of both full-time employees and contract workers to listen to and annotate speech. The tagged speech is then fed back to Alexa to improve its comprehension. And last month the Guardian reported that Google employs around 100,000 temps, vendors and contractors: literally an army of linguists working in "white collar sweatshops" to create the handcrafted data sets required for Google translate to learn dozens of languages. Not surprisingly there is a huge disparity between the wages and working conditions of these workers and Google's full time employees.

AI tagging jobs are dull, repetitive and in the case of the linguists highly skilled. But by far the worst kind of new white collar job in the AI industry is content moderation.

These tens of thousands of people, employed by third-party contractors, are required to watch and vet offensive content: hate speech, violent pornography, cruelty and sometimes murder of both animals and humans for Facebook, YouTube and other media platforms [2]. These jobs are not just dull and repetitive they are positively dangerous. Harrowing reports tell of PTSD-like trauma symptoms, panic attacks and burnout after one year, alongside micromanagement, poor working conditions and ineffective counselling. And very poor pay - typically $28,800 a year. Compare this with average annual salaries at Facebook of ~$240,000.

The big revelation to me over the past few months is the extent to which AI has a human supply chain, and I am an AI insider! The genius designers of this amazing tech rely on both huge amounts of energy and a hidden army of what Mary Gray and Siddhartha Suri call Ghost Workers.

I would like to leave you with a question: how can we, as ethical consumers, justify continuing to make use of unsustainable and unethical AI technologies?

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References:

[1] Emma Strubell, Ananya Ganesh, Andrew McCallum (2019) Energy and Policy Considerations for Deep Learning in NLP, arXiv:1906.02243
[2] Sarah Roberts (2016) Digital Refuse: Canadian Garbage, Commercial Content Moderation and the Global Circulation of Social Media’s Waste, Media Studies Publications. 14.

Relax, we're not living in a computer simulation

Since Elon Musk's recent admission that he's a simulationist, several people have asked me what I think of the proposition that we are living inside a simulation.

My view is very firmly that the Universe we are right now experiencing is real. Here are my reasons.

Firstly, Occam's razor; the principle of explanatory parsimony. The problem with the simulation argument is that it is a fantastically complicated explanation for the universe we experience. It's about as implausible as the idea that some omnipotent being created the universe. No. The simplest and most elegant explanation is that the universe we see and touch, both first hand and through our telescopes, LIGOs and Large Hadron Colliders, is the real universe and not an artifact of some massive computer simulation.

Second, is the problem of the Reality Gap. Anyone who uses simulation as a tool to develop robots is well aware that robots which appear to work perfectly well in a simulated virtual world often don't work very well at all when the same design is tested in the real robot. This problem is especially acute when we are artificially evolving those robots. The reason for these problems is that the model of the real world and the robot(s) in it inside our simulation is an approximation. The Reality Gap refers to the less-than-perfect fidelity of the simulation; a better (higher fidelity) simulator would reduce the reality gap.

Anyone who has actually coded a simulator is painfully aware of the cost, not just computational but coding costs, of improving the fidelity of the simulation - even a little bit - is very high indeed. My long experience of both coding and using computer simulations teaches me that there is a law of diminishing returns, i.e. that the cost of each additional 1% of simulator fidelity costs far more than 1%. I rather suspect that the computational and coding cost of a simulator with 100% fidelity is infinite. Rather as in HiFi audio, the amount of money you would need to spend to perfectly reproduce the sound of a Stradivarius ends up higher than the cost of hiring a real Strad and a world-class violinist to play it for you.

At this point the simulationists might argue that the simulation we are living in doesn't need to be perfect, just good enough. Good enough to do what exactly? To fool us that we're living in a simulation, or good enough to run on a finite computer (i.e. one that has finite computational power and runs at a finite speed). The problem with this argument is that every time we look deeper into the universe we see more: more galaxies, more sub-atomic particles, etc. In short we see more detail. The Voyager 1 spacecraft has left the Solar System without crashing, like Truman, into the edge of the simulation. There are no glitches like deja vu in The Matrix.

My third argument is about the computational effort, and therefore energy cost of simulation. I conjecture that to non-trivially simulate a complex system x (i.e. human), requires more energy than the real x consumes. An equation to express this inequality looks like this; how much greater depends on how high the fidelity of the simulation.



Let me explain. The average human burns around 2000 Calories a day, or about 9000 KJoules of energy. How much energy would a computer simulation of a human require, capable of doing all the same stuff (even in a virtual world) that you can in your day? Well that's impossible to estimate because we can't simulate complete human brains (let alone the rest of a human). But here's one illustration. Lee Sedol played AlphaGo a few months ago. In a single 2 hour match he burned about 170 Calories - the amount of energy you'd get from an egg sandwich. In the same 2 hours the AlphaGo machine consumed around 50,000 times more energy.

What can we simulate? The most complex organism that we have been able to simulate so far is the Nematode worm c-elegans. I previously estimated that the energy cost of simulating the nervous system of a c-elegans is (optimistically) about 9 J/hour, which is about 2000 times greater than the real nematode (0.004 J/hr).

I think there are lots of good reasons that simulating complex systems on a computer costs more energy than the same system consumes in the real world, so I'll ask you to take my word for it (I'll write about it another time). And what's more the relationship between energy cost and mass is logarithmic, following Kleiber's Law, and I strongly suspect the same law applies to scaling up computational effort as I wrote here. Thus, if the complexity of an organism o is C, then following Kleiber's Law the energy cost of simulating that organism, e will be



Furthermore, the exponent X (which in Kleiber's law is reckoned to be between 0.66 and 0.75 for animals and 1 for plants), will itself be a function of the fidelity of the simulation, hence X(F), where F is a measure of fidelity.

By using the number of synapses as a proxy for complexity and making some guesses about the values of X and F we could probably estimate the energy cost of simulating all humans on the planet (much harder would be estimating the energy cost of simulating every living thing on the planet). It would be a very big number indeed, but that's not really the point I'm making here.

The fundamental issue is this: if my conjecture that to simulate complex system x requires more energy than the real x consumes is correct, then to simulate the base level universe would require more energy than that universe contains - which is clearly impossible. Thus we - even in principle - could not simulate the whole of our own observable universe to a level of fidelity sufficient for our conscious experience. And, for the same reason, neither could our super advanced descendents create a simulation of a duplicate ancestor universe for us to (virtually) live in. Hence we are not living in such a simulation.

Estimating the energy cost of evolution

Want to create human-equivalent AI? Well, broadly speaking, there are 3 approaches open to you: design it, reverse-engineer it or evolve it. The third of these - artificial evolution - is attractive because it sidesteps the troublesome problem of having to understand how human intelligence works. It's a black box approach: create the initial conditions then let the blind watchmaker of artificial evolution do the heavy lifting. This approach has some traction. For instance David Chalmers, in his philosophical analysis of the technological singularity, writes "if we produce an AI by artificial evolution, it is likely that soon after we will be able to improve the evolutionary algorithm and extend the evolutionary process, leading to AI+". And since we can already produce simple AI by artificial evolution, then all that's needed is to 'improve the evolutionary algorithm'. Hmm. If only it were that straightforward.

About six months ago I asked myself (and anyone else who would listen): ok, but even if we had the right algorithm, what would be the energy cost of artificially evolving human-equivalent AI? My hunch was that the energy cost would be colossal; so great perhaps as to rule out the evolutionary approach altogether. That thinking, and some research, resulted in me submitting a paper to ALIFE 14. Here is the abstract:

This short discussion paper sets out to explore the question: what is the energy cost of evolving complex artificial life? The paper takes an unconventional approach by first estimating the energy cost of natural evolution and, in particular, the species Homo Sapiens Sapiens. The paper argues that such an estimate has value because it forces us to think about the energy costs of co-evolution, and hence the energy costs of evolving complexity. Furthermore, an analysis of the real energy costs of evolving virtual creatures in a virtual environment, leads the paper to suggest an artificial life equivalent of Kleiber's law - relating neural and synaptic complexity (instead of mass) to computational energy cost (instead of real energy consumption). An underlying motivation for this paper is to counter the view that artificial evolution will facilitate the technological singularity, by arguing that the energy costs are likely to be prohibitively high. The paper concludes by arguing that the huge energy cost is not the only problem. In addition we will require a new approach to artificial evolution in which we construct complex scaffolds of co-evolving artificial creatures and ecosystems.

The full proceedings of ALIFE 14 have now been published online, and my paper Estimating the Energy Cost of (Artificial) Evolution can be downloaded here.

And here's a very short (30 second) video introduction on YouTube:

My conclusion? Well I reckon that the computational energy cost of simulating and fitness testing something with an artificial neural and synaptic complexity equivalent to humans could be around 10^14 KJ, or 0.1 EJ. But evolution requires many generations and many individuals per generation, and - as I argue in the paper - many co-evolving artificial species. Also taking account of the fact that many evolutionary runs will fail (to produce smart AI), the whole process would almost certainly need to be re-run from scratch many times over. If multiplying those population sizes, generations, species and re-runs gives us (very optimistically) a factor of 1,000,000 - then the total energy cost would be 100,000 EJ. In 2010 total human energy use was about 539 EJ. So, artificially evolving human-equivalent AI would need the whole human energy generation output for about 200 years.

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The full paper reference:

Winfield AFT, Estimating the Energy Cost of (Artificial) Evolution, pp 872-875 in Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, Eds. H Sayama, J Rieffel, S Risi, R Doursat and H Lipson, MIT Press, 2014.

Related blog posts:

Why robots will not be smarter than humans by 2029