Chat GPT and the end of human culture

I had a personal situation this past year that kept me from posting much but today I decided to sit down and write something – all by myself without any help from anyone or anything. I could have enlisted the help of Chat GPT or some other large language model (LLM) but I didn’t. These posts generally start out with a foggy idea, which then take on a life of their own. Part of my enjoyment of writing these things is that I really don’t know what they will say until I’m finished. But sometime in the near future I’m pretty sure that WordPress will have a little window where you can type an idea and a LLM will just write the post for you. At first I will resist using it but one day I might not feel well and I’ll try it and like it and eventually all my posts will be created by a generative AI. Soon afterwards, the AI will learn what I like to blog about and how often I do so and it will just start posting on it’s own without my input. Maybe most or all content will be generated by an AI.

These LLMs are created by training a neural network to predict the next word of a sentence, given the previous words, sentences, paragraphs, and essentially everything that has ever been written. The machine is fed some text and produces what it thinks should come next. It then compares its prediction with the actual answer and updates its settings (connection weights) based on some score of how well it did. When fed the entire corpus of human knowledge (or at least what is online), we have all seen how well it can do. As I have speculated previously (see here), this isn’t all too surprising given that the written word is relatively new in our evolutionary history. Thus, humans aren’t really all that good at it and there isn’t all that much variety in what we write. Once an AI has the ability to predict the next word, it doesn’t take much more tinkering to make it generate an entire text. The specific technology that made this generative leap is called a diffusion model, which I may describe in more technical detail in the future. But in the simplest terms, the model finds successive small modifications to transform the initial text (or image or anything) into pure noise. The model can then be run backwards starting from random noise to create text.

When all content is generated by AI, the AI will no longer have any human data on which to further train. Human written culture will then be frozen. The written word will just consist of rehashing of previous thoughts along with random insertions generated by a machine. If the AI starts to train on AI generated text then it could leave human culture entirely. Generally, when these statistical learning machines train on their own generated data they can go unstable and become completely unpredictable. Will the AI be considered conscious by then?

The Hash, the Merkle Tree, and Bitcoin

Although cryptocurrencies have been mainstream for quite a while, I still think the popular press has not done a great job explaining the details of how they work. There are several ideas behind a cryptocurrency like Bitcoin but the main one is the concept of a cryptographic hash. In simple terms, a hash is a way to transform an input sequence of characters (i.e. a string) into an output string such that it is hard to recreate the input string from the output string. A transformation with this property is called a one-way function. It is a machine where you get an output from an input but you can’t get the input from the output and there does not exist any other machine that can get the input from the output. A hash is a one-way function where the output has a standard form, e.g. 64 characters long. So if each character is a bit, e.g. 0 or 1, then there are different possible hashes. What makes hashes useful are two properties. The first, as mentioned, is that it is a one-way function and the second is that two different inputs do not give the same hash, called collision avoidance. There have been decades of work on figuring out how to do this and institutions like the US National Institutes of Standards and Technology (NIST) actually publish hash standards like SHA-2.

Hashes are an important part of your life. If a company is responsible, then only the hash of your password is stored on their servers, and when you type your password into a website, it goes through the hashing function and the hash is checked against the stored version. That way, if there is security breach, only the hash list is stolen. If the company is very responsible, then your name and all of your information is also only stored in hash form. Part of the problem with past security breaches is that the companies stored actual information instead of hashed information. However, if you use the same password on different websites then the hash would be same if the same standard was used. Some really careful companies will “salt” your password by adding a random string to it (that is hopefullly stored separately) before hashing. Or they will rehash your hash with salt. If you had a perfect hash, then the only way to break it would be to guess different inputs and see if it matches the desired output. The so-called complex math problem that Bitcoin solves before validating a transaction (and getting a reward) is finding a hash with a certain property but more on this later.

Now, one of the problems with hashing is that you need to deal with inputs of various sizes but you want the output to have a single uniform size. So even though a hash could have enough information capacity (i.e. entropy) to encode all of the world’s information ten times over, it is computationally inconvenient to just feed the complete text of Hamlet directly into a single one-way function. This is where the concept of a Merkle tree becomes important. You start with some one-way function that takes inputs of some fixed length and it scrambles the characters in some way that is not easily reversible. If the input string is too short then you just add extra characters (called padding) but if it is too long you need to do something else. The way a Merkle tree works is to break the text into chunks of uniform size. It then hashes the first chunk, adds that to the next chunk, hash the result and repeat until you have included all the chunks. This repeated recursive hashing is the secret sauce of crypto-currencies.

Bitcoin tried to create a completely decentralized digital currency that could be secure and trusted. For a regular currency like the US dollar, the thing you are most concerned about is that the dollar you receive is not counterfeit. The way that problem is solved is to make the manufacturing process of dollar bills very difficult to reproduce. So the dollar uses special paper with special marks and threads and special fonts and special ink. There are laws against making photocopiers with a higher resolution than the smallest print on a US bill to safeguard against counterfeiting. A problem with digital currencies is that you need to prevent double spending. The way this is historically solved is to have all transactions validated by a central authority.

Bitcoin solves these problems in a decentralized system by using a public ledger, called a blockchain that is time stamped, immutable and verifiable. The block chain keeps track of every Bitcoin transaction. So if you wanted to transfer one Bitcoin to someone else then the blockchain would show that your private Bitcoin ID has one less Bitcoin and the ID of the person you transferred to would have one extra Bitcoin. It is called a blockchain because each transaction (or set of transactions) is recorded into a block, the blocks are sequential, and each block contains a hash of the previous block. To validate a transaction you would need to validate each transaction leading up to the last block to validate that the hash on each block is correct. Thus the blockchain is a Merkle tree ledger where each entry is time stamped, immutable, and verifiable. If you want to change a block you need to change all the blocks before it.

However, the blockchain is not decentralized on its own. How do you prevent two blocks with two different hashes? The way to achieve that goal is to make the hash used in each block have a special form that is hard to find. This underlies the concept of “proof of work”. Bitcoin uses a hash called SHA-256 which consists of a hexadecimal string of 64 characters (i.e. a base 16 number, usually with characters consisting of the digits 0-9 plus letters a-f). Before each block gets added to the chain, it must have a hash that has a set number of zeros at the front. In order to do this, you need to add some random numbers to the block or rearrange it so that the hash changes. This is what Bitcoin miners do. They try different variations of the block until they get a hash that has a certain number of zeros in front and then they race to see who gets it first. The more zeros you need the more guesses you need and thus the harder the computation. If it’s just one zero then one in 16 hashes will have that property and thus on average 16 tries will get you the hash and the right to add to the blockchain. Each time you require an additional zero, the number of possibilities decreases by a factor of 16 so it is 16 times harder to find one. Bitcoin wants to keep the computation time around 15 minutes so as computation speed increases it just adds another zero. The result is an endless arms race. The faster the computers get the harder the hash is to find. The incentive for miners to be fast is that they get some Bitcoins if they are successful in being the first to find a desired hash and earning the right to add a block to the chain.

The actual details for how this works is pretty complicated. All the miners (or Bitcoin nodes) must validate that the proposed block is correct and then they all must agree to add that to the chain. The way it works in a decentralized way is that the code is written so that a node will follow the longest chain. In principle, this is secure because a dishonest miner who wants to change a previous block must change all blocks following it and thus as long as there are more honest miners than dishonest ones, the dishonest ones can never catch up. However, there are issues when two miners simultaneously come up with a hash and they can’t agree on which to follow. This is called a fork and has happened at least once I believe. This gets fixed eventually because honest miners will adopt the longest chain and the chain with the most adherents will grow the fastest. However, in reality there are only a small number of miners that regularly add to the chain so we’re at a point now where a dishonest actor could possibly dominate the honest ones and change the blockchain. Proof of work is also not the only way to add to a blockchain. There are several creative ideas to make it less wasteful or even make all that computation useful and I may write about them in the future. I’m somewhat skeptical about the long term viability of Bitcoin per se but I think the concepts of the blockchain are revolutionary and here to stay.

2021-06-21: some typos fixed and clarifying text added.

Duality and computation in the MCU

I  took my kindergartener to see Avengers: Endgame recently. My son was a little disappointed, complaining that the film had too much talking and not enough fighting. To me, the immense popularity of the Marvel Cinematic Universe series and so-called science fiction/fantasy in general is an indicator of how people think they like science but really want magic. Popular science-fictiony franchises like MCU and Star Wars are couched in scientism but are often at odds with actual science as practiced today. Arthur C Clarke famously stated in his third law that “Any sufficiently advanced technology is indistinguishable from magic.” A sentiment captured in these films.

Science fiction should extrapolate from current scientific knowledge to the possible. Otherwise, it should just be called fiction. There have been a handful of films that try to do this like 2001: A Space Odyssey or more recently Interstellar and The Martian. I think there is a market for these types of films but they are certainly not as popular as the fantasy films. To be fair, neither Marvel nor Star Wars (both now owned by Disney) market themselves as science fiction as I defined it. They are intended to be mythologies a la Joseph Campbell’s Hero’s Journey. However, they do have a scientific aesthetic with worlds dominated by advanced technology.

Although I find the MCU films not overly compelling, they do bring up two interesting propositions. The first is dualism. The superhero character Ant-Man has a suit that allows him to change size and even shrink to sub-atomic scales, called the quantum realm in the films. (I won’t bother to discuss whether energy is conserved in these near instantaneous size changes, an issue that affects the Hulk as well). The film was advised by physicist Spiros Michalakis and is rife with physics terminology and concepts like quantum entanglement. One crucial concept it completely glosses over is how Ant-man maintains his identity as a person, much less his shape, when he is smaller than an atom. Even if one were to argue that one’s consciousness could be transferred to some set of quantum states at the sub-atomic scale, it would be overwhelmed by quantum fluctuations. The only self-consistent premise of Ant-Man is that the essence or soul if you wish of a person is not material. The MCU takes a definite stand for dualism on the mind-body problem, a sentiment with which I presume the public mostly agrees. 

The second is that magic has immense computational power. In the penultimate Avengers movie, the villain Thanos snaps his fingers while in possession of the complete set of infinity stones and eliminates half of all living things. (Setting aside the issue that Thanos clearly does not understand the the concept of exponential growth. If you are concerned about overpopulation, it is pointless to shrink the population and do nothing else because it will just return to its original size in short time.) What I’d like to know is who or what does the computation to carry out the command. There are at least two hard computational problems that must be solved. The first is to identify all lifeforms.  This is clearly no easy task as we to this day have no precise definition of life. Do viruses get culled by the snap? Do the population of silicon-based lifeforms of Star Trek get halved or is it only biochemical life? What algorithm does the snap use to find all the life forms? Living things on earth range in size from single cells (or viruses if you count them) all the way to 35 metre behemoths, which are comprised of over numbers of atoms. How do the stones know what scales they span in the MCU? Do photosynthetic lifeforms get spared since they don’t use many resources? What about fungi? Is the MCU actually a simulated universe where there is a continually updated census of all life? How accurate is the algorithm? Was it perfect? Did it aim for high specificity (i.e. reduce false positives so you only kill lifeforms and not non lifeforms) or high sensitivity (i.e. reduce false negatives and thus don’t miss any lifeforms). I think it probably favours sensitivity over specificity – who cares if a bunch of ammonia molecules accidentally get killed. The find-all-life problem is made much easier by proposition 1 because if all life were material then the only way to detect them would be to look for multiscale correlations between atoms (or find organic molecules if you only care about biochemical life). If each lifeform has a soul then you can simply search for “soulfulness”. The lifeforms were not erased instantly but only after a brief delay. What was happening over this delay. Is magic propagation limited by the speed of light or some other constraint? Or did the computation take time? In Endgame, the Hulk restores all the Thanos erased lifeforms and Tony Stark then snaps away Thanos and all of his allies. Where were the lifeforms after they were erased? In Heaven? In a soul repository somewhere? Is this one of the Nine Realms of the MCU? How do the stones know who is a Thanos ally? The second computation is to then decide which half to extinguish. The movie seems to imply that the choice was random so where did the randomness come from? Do the infinity stones generate random numbers? Do they rely on quantum fluctuations? Finally, in a world with magic, why is there also science? Why does the universe follow the laws of physics sometimes and magic other times. Is magic a finite resource as in Larry Niven’s The Magic Goes Away. So many questions, so few answers.

Catch-22 of our era

The screen on my wife’s iPhone was shattered this week and she had not backed up the photos. The phone seems to still be functioning otherwise so we plugged it into the computer to try to back it up but it requires us to unlock the phone and we can’t enter in the password. My wife refused to pay the 99 cents or whatever Apple charges to increase the disk space for iCloud to automatically back up the phone, so I suggested we just pay the ransom money and then the phone will back up automatically. I currently pay both Apple and Dropbox extortion money. However, since she hadn’t logged onto iCloud in maybe ever, it sent a code to her phone under the two-factor authentication scheme to type in to the website, but of course we can’t see it on her broken screen so that idea is done. We called Apple and they said you could try to change the number on her iCloud account to my phone but that was two days ago and they haven’t complied. So my wife gave up and tried to order a new phone. Under the new system of her university, which provides her phone, she can get a phone if she logs onto this site to request it. The site requires VPN and in order to get VPN she needs to, you guessed it, type in a code sent to her phone. So you need a functioning phone to order a new phone. Basically, tech products are not very good. Software still kind of sucks and is not really improving. My Apple Mac is much worse now than it was 10 years ago. I still have trouble projecting stuff on a screen. I will never get into a self driving car made by any tech company. I’ll wait for Toyota to make one; my (Japanese) car always works (my Audi was terrible).

Missing the trend

I have been fortunate to have been born at a time when I had the opportunity to witness the birth of several of the major innovations that shape our world today.  I have also managed to miss out on capitalizing on every single one of them. You might make a lot of money betting against what I think.

I was a postdoctoral fellow in Boulder, Colorado in 1993 when my very tech savvy advisor John Cary introduced me and his research group to the first web browser Mosaic shortly after it was released. The web was the wild west in those days with just a smattering of primitive personal sites authored by early adopters. The business world had not discovered the internet yet. It was an unexplored world and people were still figuring out how to utilize it. I started to make a list of useful sites but unlike Jerry Yang and David Filo, who immediately thought of doing the same thing and forming a company, it did not remotely occur to me that this activity could be monetized. Even though I struggled to find a job in 1994, was fairly adept at programming, watched the rise of Yahoo! and the rest of the internet startups, and had friends at Stanford and Silicon Valley, it still did not occur to me that perhaps I could join in too.

Just months before impending unemployment, I managed to talk my way into being the first post doc of Jim Collins, who just started as a non-tenure track research assistant professor at Boston University.  Midway through my time with Jim, we had a meeting with Charles Cantor, who was a professor at BU then, about creating engineered organisms that could eat oil. Jim subsequently recruited graduate student Tim Gardner, now CEO of Riffyn, to work on this idea. I thought we should create a genetic Hopfield network and I showed Tim how to use XPP to simulate the various models we came up with. However, my idea seemed too complicated to implement biologically so when I went to Switzerland to visit Wulfram Gerstner at the end of 1997,  Tim and Jim, freed from my meddling influence, were able create the genetic toggle switch and the field of synthetic biology was born.

I first learned about Bitcoin in 2009 and had even thought about mining some. However, I then heard an interview with one of the early developers, Gavin Andresen, and he failed to understand that because the supply of Bitcoins is finite, prices denominated in it would necessarily deflate over time. I was flabbergasted that he didn’t comprehend the basics of economics and was convinced that Bitcoin would eventually fail. Still, I could have mined thousands of Bitcoins on a laptop back then, which would be worth tens of millions today.  I do think blockchains are an important innovation and my former post-bac fellow Wally Xie is even the CEO of the blockchain startup QChain. Although I do not know where cryptocurrencies and blockchains will be in a decade, I do know that I most likely won’t have a role.

I was in Pittsburgh during the late nineties/early 2000’s in one of the few places where neural networks/deep learning, still called connectionism, was king. Geoff Hinton had already left Carnegie Mellon for London by the time I arrived at Pitt but he was still revered in Pittsburgh and I met him in London when I visited UCL. I actually thought the field had great promise and even tried to lobby our math department to hire someone in machine learning for which I was summarily dismissed and mocked. I recruited Michael Buice to work on the path integral formulation for neural networks because I wanted to write down a neural network model that carried both rate and correlation information so I could implement a correlation based learning rule. Michael even proposed that we work on an algorithm to play Go but obviously I demurred. Although, I missed out on this current wave of AI hype, and probably wouldn’t have made an impact anyway, this is the one area where I may get a second chance in the future.

 

 

The end of (video) reality

I highly recommend listening to this Radiolab podcast. It tells of new software that can create completely fabricated audio and video clips. This will take fake news to an entirely different level. It also means that citizen journalists with smartphones, police body cams, security cameras, etc. will all become obsolete. No recording can be trusted. On the other hand, we had no recording technology of any kind for almost all of human history so we will have to go back to simply trusting (or not trusting) what people say.

The robot human equilibrium

There has been some push back in the media against the notion that we will “soon” be replaced by robots, e.g. see here. But absence of evidence is not evidence of absence. Just because there seem to be very few machine induced job losses today doesn’t mean it won’t happen tomorrow or in ten years. In fact, when it does happen it probably will happen suddenly as have many recent technological changes. The obvious examples are the internet and smartphones but there are many others. We forget that the transition from vinyl records to CDs was extremely fast; then iPods and YouTube killed CDs. Video rentals became ubiquitous from nothing in just a few years and died just as fast when Netflix came along, which was then completely replaced a few years later by streaming video. It took Amazon a little longer to become dominant but the retail model that had existed for centuries has been completely upended in a decade. The same could happen with AI and robots. Unless you believe that human thought is not computable, then in principle there is nothing a human can do that a machine can’t. It could take time to set up the necessary social institutions and infrastructure for an AI takeover but once it is established the transition could be abrupt.

Even so that doesn’t mean all or even most humans will be replaced. The irony of AI, known as Moravec’s Paradox (e.g. here), is that things that are hard for humans to do, like play chess or read X-rays, are easy for machines to do and vice versa. Although drivers and warehouse workers are destined to be the first to be replaced, the next set of jobs will likely be highly paid professionals like stock brokers, accountants, doctors, and lawyers. But as the ranks of the employed start to shrink, the economy will also shrink and wages will go down (even if the displaced do eventually move on to other jobs it will take time). At some point, particularly for jobs that are easy for humans but harder for machines, humans could be cheaper than machines.  So while we can train a machine to be a house cleaner, it may be more cost effective to simply hire a person to change sheets and dust shelves. The premium on a university education will drop. The ability to sit still for long periods of time and acquire arcane specialized knowledge will simply not be that useful anymore. Centers for higher learning will become retreats for the small set of scholarly minded people who simply enjoy it.

As the economy shrinks, land prices in some areas should drop too and thus people could still eke out a living. Some or perhaps many people will opt or be pushed out of the mainstream economy altogether and retire to quasi-pre-industrial lives. I wrote about this in quasi-utopian terms in my AlphaGo post but a dystopian version is equally probable. In the dystopia, the gap between the rich and poor could make today look like an egalitarian paradise. However, unlike the usual dystopian nightmare like the Hunger Games where the rich exploit the poor, the rich will simply ignore the poor. But it is not clear what the elite will do with all that wealth. Will they wall themselves off from the rest of society and then what, engage in endless genetic enhancements or immerse themselves in a virtual reality world? I think I’d rather raise pigs and make candles out of lard.

 

 

 

 

Talk at SIAM Annual Meeting 2017

I gave an invited plenary talk at the 2017 Society of Applied and Industrial Mathematics Annual Meeting in Pittsburgh yesterday. My slides are here. I talked about some very new work on chaos and learning in spiking neural networks. My fellow Chris Kim and I were producing graphs up to a half hour before my talk! I’m quite excited about this work and I hope to get it published soon.

During my talk, I made an offhand threat that my current Mac would be the last one I buy. I made the joke because it was the first time that I could not connect to a projector with my laptop since I started using Mac almost 20 years ago. I switched to Mac from Linux back then because it was a Unix environment where I didn’t need to be a systems administrator to print and project. However, Linux has made major headway in the past two decades while Mac is backsliding. So, I’m seriously thinking of following through. I’ve been slowly getting disenchanted with Apple products over the past three years but I am especially disappointed with my new MacBook Pro. I have the one with the silly touch screen bar. The first thing the peeves me is that the activate Siri key is right next to the delete key so I accidentally hit and then have to reject Siri every five minutes. What mostly ties me to Mac right now is the Keynote presentation software, which I like(d) because it is easy to embed formulas and PDF files into. It is much harder to do the same in PowerPoint and I haven’t found an open source version that is as easy to use. However, Keynote keeps hanging on my new machine. I also get this situation where my embedded equations will randomly disappear and then reappear. Luckily I did a quick run through just before my talk and noticed that the vanished equations reappeared and I could delete them. Thus, the Keynote appeal has definitely diminished. Now, if someone would like to start an open source Keynote project with me… Finally, the new Mac does not seem any faster than my old Mac (it still takes forever to boot up) and Bard Ermentrout told me that his dynamical systems software tool XPP runs five times slower. So, any suggestions for a new machine?

AlphaGo and the Future of Work

In March of this year, Google DeepMind’s computer program AlphaGo defeated world Go champion Lee Sedol. This was hailed as a great triumph of artificial intelligence and signaled to many the beginning of the new age when machines take over. I believe this is true but the real lesson of AlphaGo’s win is not how great machine learning algorithms are but how suboptimal human Go players are. Experts believed that machines would not be able to defeat humans at Go for a long time because the number of possible games is astronomically large, moves, in contrast to chess with a paltry moves. Additionally, unlike chess, it is not clear what is a good position and who is winning during intermediate stages of a game. Thus, any direct enumeration and evaluation of possible next moves as chess computers do, like IBM’s Deep Blue that defeated Gary Kasparov, seemed to be impossible. It was thought that humans had some sort of inimitable intuition to play Go that machines were decades away from emulating. It turns out that this was wrong. It took remarkably little training for AlphaGo to defeat a human. All the algorithms used were fairly standard – supervised and reinforcement backpropagation learning in multi-layer neural networks¹. DeepMind just put them together in a clever way and had the (in retrospect appropriate) audacity to try.

The take home message of AlphaGo’s success is that humans are very, very far away from being optimal at playing Go. Uncharitably, we simply stink at Go. However, this probably also means that we stink at almost everything we do. Machines are going to take over our jobs not because they are sublimely awesome but because we are stupendously inept. It is like the old joke about two hikers encountering a bear and one starts to put on running shoes. The other hiker says: “Why are you doing that? You can’t outrun a bear.” to which she replies, “I only need to outrun you!” In fact, the more difficult a job seems to be for humans to perform, the easier it will be for a machine to do better. This was noticed a long time ago in AI research and called Moravec’s Paradox. Tasks that require a lot of high level abstract thinking like chess or predicting what movie you will like are easy for computers to do while seemingly trivial tasks that a child can do like folding laundry or getting a cookie out of a jar on an unreachable shelf is really hard. Thus high paying professions in medicine, accounting, finance, and law could be replaced by machines sooner than lower paying ones in lawn care and house cleaning.

There are those who are not worried about a future of mass unemployment because they believe people will just shift to other professions. They point out that a century ago a majority of Americans worked in agriculture and now the sector comprises of less than 2 percent of the population. The jobs that were lost to technology were replaced by ones that didn’t exist before. I think this might be true but in the future not everyone will be a software engineer or a media star or a CEO of her own company of robot employees. The increase in productivity provided by machines ensures this. When the marginal cost of production goes to zero (i.e. cost to make one more item), as it is for software or recorded media now, the whole supply-demand curve is upended. There is infinite supply for any amount of demand so the only way to make money is to increase demand.

The rate-limiting step for demand is the attention span of humans. In a single day, a person can at most attend to a few hundred independent tasks such as thinking, reading, writing, walking, cooking, eating, driving, exercising, or consuming entertainment. I can stream any movie I want now and I only watch at most twenty a year, and almost all of them on long haul flights. My 3 year old can watch the same Wild Kratts episode (great children’s show about animals) ten times in a row without getting bored. Even though everyone could be a video or music star on YouTube, superstars such as Beyoncé and Adele are viewed much more than anyone else. Even with infinite choice, we tend to do what our peers do. Thus, for a population of ten billion people, I doubt there can be more than a few million that can make a decent living as a media star with our current economic model. The same goes for writers. This will also generalize to manufactured goods. Toasters and coffee makers essentially cost nothing compared to three decades ago, and I will only buy one every few years if that. Robots will only make things cheaper and I doubt there will be a billion brands of TV’s or toasters. Most likely, a few companies will dominate the market as they do now. Even, if we could optimistically assume that a tenth of the population could be engaged in producing goods and services necessary for keeping the world functioning that still leaves the rest with little to do.

Even much of what scientists do could eventually be replaced by machines. Biology labs could consist of a principle investigator and robot technicians. Although it seems like science is endless, the amount of new science required for sustaining the modern world could diminish. We could eventually have an understanding of biology sufficient to treat most diseases and injuries and develop truly sustainable energy technologies. In this case, machines could be tasked to keep the modern world up and running with little need of input from us. Science would mostly be devoted to abstract and esoteric concerns.

Thus, I believe the future for humankind is in low productivity occupations – basically a return to pre-industrial endeavors like small plot farming, blacksmithing, carpentry, painting, dancing, and pottery making, with an economic system in place to adequately live off of this labor. Machines can provide us with the necessities of life while we engage in a simulated 18th century world but without the poverty, diseases, and mass famines that made life so harsh back then. We can make candles or bread and sell them to our neighbors for a living wage. We can walk or get in self-driving cars to see live performances of music, drama and dance by local artists. There will be philosophers and poets with their small followings as they have now. However, even when machines can do everything humans can do, there will still be a capacity to sustain as many mathematicians as there are people because mathematics is infinite. As long as P is not NP, theorem proving can never be automated and there will always be unsolved math problems.  That is not to say that machines won’t be able to do mathematics. They will. It’s just that they won’t ever be able to do all of it. Thus, the future of work could also be mathematics.

1. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

The simulation argument made quantitative

Elon Musk, of Space X, Tesla, and Solar City fame, recently mentioned that he thought the the odds of us not living in a simulation were a billion to one. His reasoning was based on extrapolating the rate of improvement in video games. He suggests that soon it will be impossible to distinguish simulations from reality and in ten thousand years there could easily be billions of simulations running. Thus there are a billion more simulated universes than real ones.

This simulation argument was first quantitatively formulated by philosopher Nick Bostrom. He even has an entire website devoted to the topic (see here). In his original paper, he proposed a Drake-like equation for the fraction of all “humans” living in a simulation:

where is the fraction of human level civilizations that attain the capability to simulate a human populated civilization, is the fraction of these civilizations interested in running civilization simulations, and is the average number of simulations running in these interested civilizations. He then argues that if is large, then either or . Musk believes that it is highly likely that is large and is not small so, ergo, we must be in a simulation. Bostrom says his gut feeling is that is around 20%. Steve Hsu mocks the idea (I think). Here, I will show that we have absolutely no way to estimate our probability of being in a simulation.

The reason is that Bostrom’s equation obscures the possibility of two possible divergent quantities. This is more clearly seen by rewriting his equation as

where is the number of non-sim civilizations and is the number of sim civilizations. (Re-labeling  and as people or universes does not change the argument). Bostrom and Musk’s observation is that once a civilization attains simulation capability then the number of sims can grow exponentially (people in sims can run sims and so forth) and thus can overwhelm and ergo, you’re in a simulation. However, this is only true in a world where is not growing or growing slowly. If is also growing exponentially then we can’t say anything at all about the ratio of to .

I can give a simple example.  Consider the following dynamics

is being created by but both are both growing exponentially. The interesting property of exponentials is that a solution to these equations for is

where I have chosen convenient initial conditions that don’t affect the results. Even though is growing exponentially on top of an exponential process, the growth rates of and are the same. The probability of being in a simulation is then

and we have no way of knowing what this is. The analogy is that you have a goose laying eggs and each daughter lays eggs, which also lay eggs. It would seem like there would be more eggs from the collective progeny than the original mother. However, if the rate of egg laying by the original mother goose is increasing exponentially then the number of mother eggs can grow as fast as the number of daughter, granddaughter, great…, eggs. This is just another example of how thinking quantitatively can give interesting (and sometimes counterintuitive) results. Until we have a better idea about the physics underlying our universe, we can say nothing about our odds of being in a simulation.

Addendum: One of the predictions of this simple model is that there should be lots of pre-sim universes. I have always found it interesting that the age of the universe is only about three times that of the earth. Given that the expansion rate of the universe is actually increasing, the lifetime of the universe is likely to be much longer than the current age. So, why is it that we are alive at such an early stage of our universe? Well, one reason may be that the rate of universe creation is very high and so the probability of being in a young universe is higher than being in an old one.

Addendum 2: I only gave a specific solution to the differential equation. The full solution has the form .  However, as long as , the first term will dominate.

Addendum 3: I realized that I didn’t make it clear that the civilizations don’t need to be in the same universe. Multiverses with different parameters are predicted by string theory.  Thus, even if there is less than one civilization per universe, universes could be created at an exponentially increasing rate.

 

What Uber doesn’t get

You may have heard that ride hailing services Uber and Lyft have pulled out of Austin, TX because they refuse to be regulated. You can read about the details here. The city wanted to fingerprint drivers, as they do for taxis, but Uber and Lyft forced a referendum on the city to make them exempt or else they would leave. The city voted against them. I personally use Uber and really like it but what I like about Uber has nothing to do with Uber per se or regulation. What I like is 1) no money needs to be exchanged especially the tip and 2) the price is essentially fixed so it is in the driver’s interest to get me to my destination as fast as possible. I have been taken on joy rides far too many times by taxi drivers trying to maximize the fare and I never know how much to tip. However, these are things that regulated taxis could implement and should implement. I do think it is extremely unfair that Uber can waltz into a city like New York and compete against highly regulated taxis, who have paid as much as a million dollars for the right to operate. Uber and Lyft should collaborate with existing taxi companies rather than trying to put them out of business. There was a reason to regulate taxis (e.g. safety, traffic control, fraud protection), and that should apply whether I hail a cab on the street or I use a smartphone app.

Worst machine in the world

I really hate leaf blowers. I think they are a huge waste of energy. They mostly make noise and heat and perform a task that perfectly good tools (i.e. broom and rake) can already do extremely well.  I don’t believe they save much time. (Yes, I have raked a lawn full of leaves before.)

 

Phasers on stun

The recent controversy over police shootings of unarmed citizens has again stirred up the debate over gun control. However, Shashaank Vattikuti points out that there is another option and that is for the police to carry nonlethal weapons like phasers with a stun option. Although, an effective long range nonlethal weapon currently does not exist (tasers just don’t cut it), a billionaire like Mark Zuckerberg, Peter Thiel, or Elon Musk could start a company to develop one. New York Times columnist Joe Nocera has suggested that Michael Bloomberg buy a gun company. There are so many guns already in existence that barring an unlikely confiscation scheme there is probably no way to get rid of them. The only way to reduce gun violence at this point is for a superior technology to make them obsolete. Hobbyists and collectors would still own guns, just as there are sword collectors, but those who own guns for protection would probably slowly switch over. However, the presence of a nonlethal option could lead to more people shooting each other so strong laws regarding their use would need to accompany their introduction.

 

 

 

 

Are we in a fusion renaissance?

Fusion is a potentially unlimited source of non-carbon emitting energy. It requires the mashing together of small nuclei such as deuterium and tritium to make another nucleus and a lot of leftover energy. The problem is that nuclei do not want to be mashed together and thus to achieve fusion you need something to confine high energy nuclei for a long enough time. Currently, there are only two methods that have successfully demonstrated fusion: 1) gravitational confinement as in the center of a star, and 2) inertial confinement as in a nuclear bomb. In order to get nuclei at high enough energy to overcome the energy barrier for a fusion reaction, electrons can no longer be bound to nuclei to form atoms. A gas of quasi-neutral hot nuclei and electrons is called a plasma and has often been dubbed the fourth state of matter. Hence, the physics of fusion is mostly the physics of plasmas.

My PhD work was in plasma physics and although my thesis ultimately dealt with chaos in nonlinear partial differential equations, my early projects were tangentially related to fusion. At that time there were two approaches to attaining fusion, one was to try to do controlled inertial confinement by using massive lasers to implode a tiny pellet of fuel and the second was to use magnetic confinement in a tokamak reactor. Government sponsored research has been focused almost exclusively on these two approaches for the past forty years. There is a huge laser fusion lab at Livermore and an even bigger global project for magnetic confinement fusion in Cadarache France, called ITER. As of today, neither has proven that they will ever be viable sources of energy although there is evidence of break even where the reactors produce more energy than is put in.

However, these approaches may not ultimately be viable and there really has not been much research funding to pursue alternative strategies. This recent New York Times article reports on a set of privately funded efforts to achieve fusion backed by some big names in technology including Paul Allen, Jeff Bezos and Peter Thiel. Although there is well deserved skepticism for the success of these companies,  (I’m sure my thesis advisor Abe Bers would have had some insightful things to say about them), the time may be ripe for new approaches. In an impressive talk I heard many years ago, roboticist Rodney Brooks remarked that Moore’s Law has allowed robotics to finally be widely available because you could use software to compensate for hardware. Instead of requiring cost prohibitive high precision motors, you could use cheap ones and use software to control them. The hybrid car is only possible because of the software to decide when to use the electric motor and when to use the gas engine. The same idea may also apply to fusion. Fusion is so difficult because plasmas are inherently unstable. Most of the past effort has been geared towards designing physical systems to contain them. However, I can now imagine using software instead.

Finally, government attempts have mostly focused on using a Deuterium-Tritium fusion reaction because it has the highest yield. The problem with this reaction is that it produces a neutron, which then destroys the reactor. However, there are reactions that do not produce neutrons (see here). Abe used to joke that that we could mine the moon for Helium 3 to use in a Deuterium-Helium 3 reactor. So, although we may never have viable fusion on earth, it could be a source of energy on Elon Musk’s moon base, although solar would probably be a lot cheaper.

The Drake equation and the Cambrian explosion

This summer billionaire Yuri Milner announced that he would spend upwards of 100 million dollars to search for extraterrestrial intelligent life (here is the New York Times article). This quest to see if we have company started about fifty years ago when Frank Drake pointed a radio telescope at some stars. To help estimate the number of possible civilizations, , Drake wrote down his celebrated equation,

where is the rate of star formation, is the fraction of stars with planets, is the average number of planets per star that could support life, fraction of planets that develop life, fraction of those planets that develop intelligent life, fraction of civilizations that emit signals, and is the length of time civilizations emit signals.

The past few years have demonstrated that planets in the galaxy are likely to be plentiful and although the technology to locate earth-like planets does not yet exist, my guess is that they will also be plentiful. So does that mean that it is just a matter of time before we find ET? I’m going to come on record here and say no. My guess is that life is rare and intelligent life may be so rare that there could only be one civilization at a time in any given galaxy.

While we are now filling in the numbers for the left side of Drake’s equation, we have absolutely no idea about the right side of the equation. However, I have good reason to believe that it is astronomically small and that reason is statistical independence. Although Drake characterized the probability of intelligent life into the probability of life forming times the probability it goes on to develop extra-planetary communication capability, there are actually a lot of factors in between. One striking example is the probability of the formation of multi-cellular life. In earth’s history, for the better part of three and a half billion years we had mostly single cellular life and maybe a smattering of multicellular experiments. Then suddenly about half a billion years ago, we had the Cambrian Explosion where multicellular animal life from which we are descended suddenly came onto the scene. This implies that forming multicellular life is extremely difficult and it is easy to envision an earth where it never formed at all.

We can continue. If it weren’t for an asteroid impact, the dinosaurs may never have gone extinct and mammals may not have developed. Even more recently, there seem to have been many species of advanced primates yet only one invented radios. Agriculture only developed ten thousand years ago, which meant that modern humans took about a hundred thousand years to discover it and only in one place. I think it is equally plausible that humans could have gone extinct like all of our other australopithecus and homo cousins. Life in the sea has existed much longer than life on land and there is no technologically advanced sea creature although I do think octopuses, dolphins and whales are intelligent.

We have around 100 billion stars in the galaxy and let’s just say that each has a habitable planet. Well, if the probability of each stage of life is one in a billion and if we need say three stages to attain technology then the probability of finding ET is one in . I would say that this is an optimistic estimate. Probabilities get small really quickly when you multiply them together. The probability of single cellular life will be much higher. It is possible that there could be hundred planets in our galaxy that have life but the chance that one of those is within a hundred light years will again be very low. However, I do think it is a worthwhile exercise to look for extracellular life, especially for oxygen or other life emitting gases in the atmosphere of exoplanets. It could tell us a lot about biology on earth.

2015-10-1: I corrected a factor of 10 error in some of the numbers.

How we got to now

I’ve been watching the highly entertaining PBS series “How we got to now” based on the book and hosted by Steven Johnson.  You can catch the episodes here for a limited time.  Hurry though because the first two episodes expire tomorrow.

The perils of Word

Many biology journals insist on receiving manuscripts in Microsoft Word prior to publication. Even though this probably violates some anti-trust law, I generally comply, to the point of painfully converting Latex manuscripts into Word on more than one occasion. Word is particularly unpleasant when writing papers with equations. Although the newer versions have a new equation editing system, I don’t use it because once in a past submission, a journal forced me to convert all the equations to the old equation editor system (the poor person’s version of MathType). It worked reasonably well in Word versions before 2008 but has now become very buggy in Word 2011. For instance, when I double-click on an equation to edit it, a blank equation editor window also pops up, which I have to close in order for the one I wanted to work. Additionally, when I reopen papers saved in the .docx format, the equations lose their alignment.  Instead of the center of the equation aligned to a line, the base of the equation is aligned making inline equations float above the line. Finally, a big problem is equation numbering.  Latex has a nice system where you give each equation a label and then it assigns numbers automatically when you compile. This way you can insert and delete equations without having to renumber them all. Is there a way you can do this in Word? Am I the only one with these problems? Are there work arounds?

Marc Andreesen on EconTalk

If you have any interest in technology and the internet then you should definitely listen to this EconTalk podcast with Marc Andreesen, who wrote the first web browser Mosaic that led to the explosive growth of the internet. He has plenty of insightful things to say.  I remember first seeing Mosaic in 1994 as a postdoc in Boulder, Colorado. There I was, doing research that involved programming in C and C++. I was not really happy with what I was doing. I was having a hard time finding the next job. I was one of the first to play around with HTML, and it never occurred to me once that I could pack my bags, move to Silicon Valley, and try to get involved in the burgeoning tech revolution. It just makes me wonder what other obvious things I’m missing right now.

Addendum, 2014-6-5:  Actually, it may have been 1993 that I first saw Mosaic.

The ultimate pathogen vector

If civilization succumbs to a deadly pandemic, we will all know what the vector was. Every physician, nurse, dentist, hygienist, and health care worker is bound to check their smartphone sometime during the day before, during, or after seeing a patient and they are not sterilizing it afterwards.  The fully hands free smartphone could be the most important invention of the 21st century.

America’s Cup 2013

Today may be the last race for the America’s Cup yacht series between the US and New Zealand.   Here are the highlights from the last race.

It is a best of 17 series and New Zealand has 8 wins so today may be the last chance to watch these hundred million dollar multihull yachts fly around San Francisco harbour at close to 50 miles per hour.  All the races are posted on You Tube.