1957: Sputnik, atomic cooking, machines that code & central dogmas

What have we learned since 1957? Did we predict what it would be? And what does it tell us about our future?

Some notes for the panel discussion “‘We’ve never had it so good’ – how does the world today compare to 1957?” 11 May 2015 by Dr Anders Sandberg, James Martin Research Fellow at the Future of Humanity Institute, Oxford Martin School, Oxford University.

Taking the topic “how does the world today compare to 1957?” a bit literally and with a definite technological bent, I started reading old issues of Nature to see what people were thinking about back then.

Technology development

Space

In 1957 the space age began.

Sputnik 1, the first artificial satellite, was launched on 4 October 1957. On November 3 Sputnik 2 was launched, with Laika, the first animal to orbit the Earth. The US didn’t quite manage to follow up within the year, but succeeded with Explorer 1 in January 1958.

Earth rising over the Moon from Apollo 8.

Right now, Voyager 1 is 19 billion km from earth, leaving the solar system for interstellar space. Probes have visited all the major bodies of the solar system. There are several thousand satellites orbiting Earth and other bodies. Humans have set their footprint on the Moon – although the last astronaut on the moon left closer to 1957 than the present.

There is a pair of surprises here. The first is how fast humanity went from primitive rockets and satellites to actual moon landings – 12 years. The second is that the space age did not grow into a roaring colonization of the cosmos, despite the confident predictions of nearly anybody in the 1950s. In many ways space embodies the surprises of technological progress – it can go both faster and slower than expected, often at the same time.

Nuclear

1957 also marks the first time that power was generated from a commercial nuclear plant, at Santa Susana, California, and the first full-scale nuclear power plant (Shippingport, Pennsylvania). Now LA housewives were cooking with their friend the atom! Ford announced their Nucleon atomic concept car 1958 – whatever the future held, it was sure to be nuclear powered!

Nuclearcooking LA times

Except that just like the Space Age the Atomic Age turned out to be a bit less pervasive than imagined in 1957.

World energy usage by type. From Our World In Data.

One reason might be found in the UK Windscale nuclear power plant accident on 10th October 1957. Santa Susana also turned into an expensive superfund clean-up site. Making safe and easily decommissioned nuclear plants turned out to be far harder than imagined in the 1950s. Maybe, as Freeman Dyson has suggested[1], the world simply choose the wrong branch of the technology tree to walk down, selecting the big and complex plants suitable for nuclear weapons isotopes rather than small, simple and robust plants. In any case, today nuclear power is struggling both against cost and broadly negative public perceptions.

Computers

First Fortran compiler. Picture from Grinnel College.

In April 1957 IBM sells the first compiler for the FORTRAN scientific programming language, as a hefty package of punched cards. This represents the first time software allowing a computer to write software is sold.

The term “artificial intelligence” had been invented the year before at the famous Dartmouth conference on artificial intelligence, which set out the research agenda to make machines that could mimic human problem solving. Newell, Shaw and Simon demonstrated the General Problem Solver (GPS) in 1957, a first piece of tangible progress.

While the Fortran compiler was a completely independent project it does represent the automation of programming. Today software development involves using modular libraries, automated development and testing: a single programmer can today do projects far outside what would have been possible in the 1950s. Cars run software on the order of 100s of million lines of code, and modern operating systems easily run into the high tens of millions of lines of code[2].

Moore's law, fitted with Jacknifed sigmoids. Green lines mark 98% confidence interval. Data from Nordhaus. — Moore’s law, fitted with Jacknifed sigmoids. Green lines mark 98% confidence interval. Data from Nordhaus.

In 1957 Moore’s law was not yet coined as a term, but the dynamics was already ongoing: computer operations per second per dollar was increasing exponentially (this is the important form of Moore’s law, rather than transistor density – few outside the semiconductor industry actually care about that). Today we can get about 440 billion times as many computations per second per dollar now as in 1957. Similar laws apply to storage (in 1956 IBM shipped the first hard drive in the RAMAC 305 system. The drive held 5MB of data at $10,000 a megabyte, as big as two refrigerators), memory prices, sizes of systems and sensors.

This tremendous growth have not only made complex and large programs possible, or enabled supercomputing (today’s best supercomputer is about 67 billion times more powerful than the first ones in 1964), but has also allowed smaller and cheaper devices that can be portable and used everywhere. The performance improvement can be traded for price and size.

In 1957 the first electric watch – the Hamilton Ventura – was sold. Today we have the Apple watch. Both have the same main function, to show off the wealth of their owner (and incidentally tell time), but the modern watch is also a powerful computer able to act as a portal into our shared information world. Embedded processors are everywhere, from washing machines to streetlights to pacemakers.

Why did the computers take off? Obviously there was a great demand for computing, but the technology also contained the seeds of making itself more powerful, more flexible, cheaper and useful in ever larger domains. As Gordon Bell noted in 1970, “Roughly every decade a new, lower priced computer class forms based on a new programming platform, network, and interface resulting in new usage and the establishment of a new industry.”[3]

At the same time, artificial intelligence has had a wildly bumpy ride. From confident predictions of human level intelligence within a generation to the 1970s “AI winter” when nobody wanted to touch the overhyped and obsolete area, to the current massive investments in machine learning. The problem was to a large extent that we could not tell how hard problems in the field were: some like algebra and certain games yielded with ease, others like computer vision turned out to be profoundly hard.

Biotechnology

In 1957 Francis Crick laid out the “central dogma of molecular biology”, which explained the relationship between DNA, RNA, and proteins (DNA is translated into RNA, which is translated into proteins, and information only flows this way). The DNA structure had been unveiled four years earlier and people were just starting to figure out how genetics truly worked.

(Incidentally, the reason for the term “dogma” was that Crick, a nonbeliever, thought the term meant something that was unsupported by evidence and just had to be taken by faith, rather than the real meaning of the term, something that has to be believed no matter what. Just like “black holes” and the “big bang”, names deliberately coined to mock, it stuck.)

It took time to learn how to use DNA, but in the 1960s we learned the language of the genetic code, by the early 1970s we learned how to write new information into DNA, by the 1980s commercial applications began, by the 1990s short genomes were sequenced…

Price for DNA sequencing and synthesis. From Rob Carlson.

Today we have DNA synthesis machines that can be bought on eBay, unless you want to order your DNA sequence online and get a vial in the mail. Conversely, you can send off a saliva sample and get a map (or the entire sequence) of your genome back. The synthetic biology movement are sharing “biobricks”, modular genetic devices that can be combined and used to program cells. Students have competitions in genetic design.

The dramatic fall in price of DNA sequencing and synthesis mimics Moore’s law and is in some sense a result of it: better computation and microtechnology enables better biotechnology. Conversely, the cheaper it is, the more uses can be found – from marking burglars with DNA spray to identifying the true origins of sushi. This also speeds up research, leading to discoveries of new useulf tricks, for example leading to the current era of CRISPR/Cas genetic editing which promises vastly improved precision and efficiency over previous methods.

Average corn yields over time. Image from Biodesic.

Biotechnology is of course more than genetics. One of the most important aspects of making the world better is food security. The gains in agricultural productivity have also been amazing. One of the important take-home messages in the above graph is that the improvement began before we started to explicitly tinker with the genes: crossing methods in the post-war era already were improving yields. Also, the Green Revolution in the 1960s was driven not just by better varieties, but by changes in land use, irrigation, fertilization and other less glamorous – but important – factors. The utility of biotechnology in the large is strongly linked to how it fits with the infrastructure of society.

Predicting technology

"Science on the March" (Alexander Leydenfrost) — “Science on the March” (Alexander Leydenfrost)

Learning about what is easy and hard requires experience. Space was on one hand easy – it only took 17 years from Sputnik before the astronauts left the moon – but making it sustained turned out to be hard. Nuclear power was easy to make, but hard to make safe enough to be cheap and acceptable. Software has taken off tremendously, but compilers have not turned into “do what I mean” – yet routine computer engineering is regularly producing feats beyond belief that have transformed our world. AI has died the hype death several times, yet automated translation, driving, games, logistics and information services are major business today. Biotechnology had a slow ramp-up, then erupted and now schoolchildren modifying genes – yet heavy resistance holds it back, largely not because of any objective danger but because of cultural views.

If we are so bad at predicting what future technology will transform the world, what are we to do when we are searching for the Next Big Thing to solve our crises? The best approach is to experiment widely. Technologies with low thresholds of entry – such as software and now biotechnology – allow for more creative exploration. More people, more approaches and more aims can be brought to bear, and will find unexpected use for them.

The main way technologies become cheap and ubiquitous is that they are mass produced. As long as spacecraft and nuclear reactors nearly one-offs they will remain expensive. But as T. P. Wright observed, the learning curve makes each new order a bit cheaper or better. If we can reach the point where many are churned out they will not just be cheap, they will also be used for new things. This is the secret of the transistor and electronic circuit: by becoming so cheap they could be integrated anywhere they also found uses everywhere.

So the most world-transforming technologies are likely to be those that can be mass-produced, even if they from the start look awfully specialized. CCDs were once tools for astronomy, and now are in every camera and phone. Cellphones went from a moveable telephone to a platform for interfacing with the noosphere. Expect the same from gene sequencing, cubesats and machine learning. But predicting what technologies will dominate the world in 60 years’ time will not be possible.

Are we better off?

Having more technology, being able to reach higher temperatures, lower pressures, faster computations or finer resolutions, does not equate to being better off as humans.

Healthy and wise

Life expectancy (male and female) in England and Wales.

Perhaps the most obvious improvement has been health and life expectancy. Our “physiological capital” has been improving significantly. Life expectancy at birth has increased from about 70 in 1957 to 80 at a steady pace. The chance of living until 100 went up from 12.2% in 1957 to 29.9% in 2011[4].

The most important thing here is that better hygiene, antibiotics, and vaccinations happened before 1957! They were certainly getting better afterwards, but the biggest gains were likely early. Since 1957 it is likely that the main causes have been even better nutrition, hygiene, safety, early detection of many conditions, as well as reduction of risk factors like smoking.

Advanced biomedicine certainly has a role here, but it has been smaller than one might be led to think until about the 1970s. “Whether or not medical interventions have contributed more to declining mortality over the last 20 years than social change or lifestyle change is not so clear.”[5] This is in many ways good news: we may have a reserve of research waiting to really make an impact. After all, “evidence based medicine”, where careful experiment and statistics are applied to medical procedure, began properly in the 1970s!

A key factor is good health habits, underpinned by research, availability of information, and education level. These lead to preventative measures and avoiding risk factors. This is something that has been empowered by the radical improvements in information technology.

Consider the cost of accessing an encyclopaedia. In 1957 encyclopaedias were major purchases for middle class families, and if you didn’t have one you better have bus money to go to the local library to look up their copy. In the 1990s the traditional encyclopaedias were largely killed by low-cost CD ROMs… before Wikipedia appeared. Wikipedia is nearly free (you still need an internet connection) and vastly more extensive than any traditional encyclopaedia. But the Internet is vastly larger than Wikipedia as a repository of knowledge. The curious kid also has the same access to the ArXiv preprint server as any research physicist: they can reach the latest paper at the same time. Not to mention free educational courses, raw data, tutorials, and ways of networking with other interested people.

Wikipedia is also good demonstration of how the rules change when you get something cheap enough – having volunteers build and maintain something as sophisticated as an encyclopaedia requires a large and diverse community (it is often better to have many volunteers than a handful of experts, as competitors like Scholarpedia have discovered), and this would not be possible without easy access. It also illustrates that new things can be made in “alien” ways that cannot be predicted before they are tried.

Risk

But our risks may have grown too.

1957 also marks the launch of the first ICBM, a Soviet R-7. In many ways it is intrinsically linked to spaceflight: an ICBM is just a satellite with a ground-intersecting orbit. If you can make one, you can build the other.

By 1957 the nuclear warhead stockpiles were going up exponentially and had reached 10,000 warheads, each potentially able to destroy a city. Yields of thermonuclear weapons were growing larger, as imprecise targeting made it reasonable to destroy large areas in order to guarantee destruction of the target.

Nuclear warhead stockpiles. From the Center of Arms Control and Non-Proliferation.

While the stockpiles have decreased and the tensions are not as high as during the peak of the cold war in the early 80s, we have more nuclear powers, some of which are decidedly unstable. The intervening years have also shown a worrying number of close calls – not just the Cuban Missile crisis but many other under-reported crises, flare-ups and technical mishaps (Indeed, in May 22 1957 a 42,000-pound hydrogen bomb accidentally fell from a bomber near Albuquerque). The fact that we got out of the Cold War unscathed is surprising – or maybe not, since we would not be having this discussion if it had turned hot.

The biological risks are also with us. The Asian Bird Flu pandemic in 1957 claimed over 150,000 lives world-wide. Current gain-of-function research may, if we are very unlucky, lead to a man-made pandemic with a worse outcome. The paradox here is that this particular research is motivated by a desire to understand how bird flu can make the jump from birds to an infectious human pathogen: we need to understand this better, yet making new pathogens may be a risky path.

The SARS and Ebola crises show that we both have become better at handling a pandemic emergency, but also have far to go. It seems that the natural biological risk may have gone down a bit because of better healthcare (and increased a bit due to more global travel), but the real risks from misuse of synthetic biology are not here yet. While biowarfare and bioterrorism are rare, they can have potentially unbounded effects – and cheaper, more widely available technology means it may be harder to control what groups can attempt it.

1957 also marks the year when Africanized bees escaped in Brazil, becoming one of the most successful and troublesome invasive (sub)species. Biological risks can be directed to agriculture or the ecosystem too. Again, the intervening 60 years have shown a remarkably mixed story: on one hand significant losses of habitat, the spread of many invasive species, and the development of anti-agricultural bioweapons. On the other hand a significant growth of our understanding of ecology, biosafety, food security, methods of managing ecosystems and environmental awareness. Which trend will win out remains uncertain.

The good news is that risk is not a one-way street. We likely have reduced the risk of nuclear war since the heights of the Cold War. We have better methods of responding to pandemics today than in 1957. We are aware of risks in a way that seems more actionable than in the past: risk is something that is on the agenda (sometimes excessively so).

Coordination

1957/1958 was the International Geophysical Year. The Geophysical Year saw the US and Soviet Union – still fierce rivals – cooperate on understanding and monitoring the global system, an ever more vital part of our civilization.

1957 was also the year of the treaty of Rome, one of the founding treaties of what would become the EU. For all its faults the European Union demonstrates that it is possible through trade to stabilize a region that had been embroiled in wars for centuries.

Number of international treaties over time. Data from Wikipedia.

The number of international treaties has grown from 18 in 1957 to 60 today. While not all represent sterling examples of cooperation they are a sign that the world is getting somewhat more coordinated.

Globalisation means that we actually care about what goes on in far corners of the world, and we will often hear about it quickly. It took days after the Chernobyl disaster in 1986 before it was confirmed – in 2011 I watched the Fukushima YouTube clip 25 minutes after the accident, alerted by Twitter. It has become harder to hide a problem, and easier to request help (overcoming one’s pride to do it, though, remains as hard as ever).

The world on 1957 was closed in many ways: two sides of the Cold War, most countries with closed borders, news traveling through narrow broadcasting channels and transport/travel hard and expensive. Today the world is vastly more open, both to individuals and to governments. This has been enabled by better coordination. Ironically, it also creates more joint problems requiring joint solutions – and the rest of the world will be watching the proceedings, noting lack of cooperation.

Final thoughts

The real challenges for our technological future are complexity and risk.

We have in many ways plucked the low-hanging fruits of simple, high-performance technologies that vastly extend our reach in energy, material wealth, speed and so on, but run into subtler limits due to the complexity of the vast technological systems we need. The problem of writing software today is not memory or processing speed but handling a myriad of contingencies in distributed systems subject to deliberate attacks, emergence, localization, and technological obsolescence. Biotechnology can do wonders, yet has to contend with organic systems that have not been designed for upgradeability and spontaneously adapt to our interventions. Handling complex systems is going to be the great challenge for this century, requiring multidisciplinary research and innovations – and quite likely some new insights on the same level as the earth-shattering physical insights of the 20^th century.

More powerful technology is also more risky, since it can have greater consequences. The reach of the causal chains that can be triggered with a key press today are enormously longer than in 1957. Paradoxically, the technologies that threaten us also have the potential to help us reduce risk. Spaceflight makes ICBMs possible, but allows global monitoring and opens the possibility of becoming a multi-planetary species. Biotechnology allows for bioweapons, but also disease surveillance and rapid responses. Gene drives can control invasive species and disease vectors, or sabotage ecosystems. Surveillance can threaten privacy and political freedom, yet allow us to detect and respond to collective threats. Artificial intelligence can empower us, or produce autonomous technological systems that we have no control over. Handling risk requires both having an adequate understanding of what matters, designing the technologies, institutions or incentives that can reduce the risk – and convincing the world to use them.

The future of our species depends on what combination of technology, insight and coordination ability we have. Merely having one or two of them is not enough: without technology we are impotent, without insight we are likely to go in the wrong direction, and without coordination we will pull apart.

Fortunately, since 1957 I think we have not just improved our technological abilities, but we have shown a growth of insight and coordination ability. Today we are aware of global environmental and systemic problems to a new degree. We have integrated our world to an unprecedented degree, whether through international treaties, unions like the EU, or social media. We are by no means “there” yet, but we have been moving in the right direction. Hence I think we never had it so good.

[1]Freeman Dyson, Imagined Worlds. Harvard University Press (1997) P. 34-37, p. 183-185

[2] http://www.informationisbeautiful.net/visualizations/million-lines-of-code/

[3] https://en.wikipedia.org/wiki/Bell%27s_law_of_computer_classes

[4] https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/223114/diffs_life_expectancy_20_50_80.pdf

[5] http://www.beyondcurrenthorizons.org.uk/review-of-longevity-trends-to-2025-and-beyond/

Tying up loose ties

Our paper on tie knot classification is finally officially published: Hirsch D, Markström I, Patterson ML, Sandberg A, Vejdemo-Johansson M.(2015) More ties than we thought. PeerJ Computer Science 1:e2.

Besides the paper and its supplementary code, there is also a random tie knot generator and a tutorial by Mikael about how to read the notation.

The classification of tie knots is not in itself important, but having a nice notation helps for specifying how to tie them. And the links to languages and finite state machines are cool. The big research challenge is understanding how knot façades are to be modelled and judged.

Brewing more than booze

Over on Practical Ethics I blog about how to handle production of opiates from bioengineered yeast.

The basic problem is that opiates seem to be unusually harmful (rather nasty dependency, social withdrawal and risky methods of administration), yet restricting access looks hard in the long run. I don’t subscribe to the view that mere exposure will turn all people into addicts (it looks like it is a subset of people who are vulnerable), but there is a fair bit of harm here that likely is not outweighed by cheapness and better quality. Yet proposed methods restricting access to the modified yeast are unlikely to work in the long run, and may some bad effects on their own.

My own solution is to recognize that in 10-20 years it will be possible to brew many strong drugs discreetly at home, and that we need to reduce the harm from this by developing other technologies that make them less problematic. It might sound wussy and complex compared to the more easily actionable targets suggested in the article, but I think it has a greater chance of actually reducing harms in the long run than policies that merely delay the broad arrival of microbrew drugs.

Awesome blogs

I recently discovered Alex Wellerstein’s excellent blog Restricted data: the nuclear secrecy blog. I found it while looking for nuclear stockpiles data, but was drawn in by a post on the evolution of nuclear yield to mass. Then I started reading the rest of it. And finally, when reading this post about the logo of the IAEA I realized I needed to mention to the world how good it is. Be sure to test the critical assembly simulator to learn just why critical mass is not the right concept.

Another awesome blog is Almost looks like work by Jasmcole. I originally found it through a wonderfully over the top approach to positioning a wifi router (solving Maxwell’s equations turns out to be easier than the Helmholz equation!). But there are many other fascinating blog essays on physics, mathematics, data visualisation, and how to figure out propeller speeds from camera distortion.

Baby interrupted

Francesca Minerva and me have a new paper out: Cryopreservation of Embryos and Fetuses as a Future Option for Family Planning Purposes (Journal of Evolution and Technology – Vol. 25 Issue 1 – April 2015 – pgs 17-30).

Basically, we analyse the ethics of cryopreserving fetuses, especially as an alternative to abortion. While technologically we do not have any means to bring a separated (yet alone cryopreserved) fetus to term yet, it is not inconceivable that advances in ectogenesis (artificial wombs) or biotechnological production of artificial placentas allowing reinplantation could be achieved. And a cryopreserved fetus would have all the time in the world, just like an adult cryonics patient.

It is interesting to see how many of the standard ethical arguments against abortion fare when dealing with cryopreservation. There is no killing, personhood is not affected, there is no loss of value of the future – just a long delay. One might be concerned that fetuses will not be reinplanted but just left in limbo forever, but clearly this is a better state than being irreversibly aborted: cryopreservation can (eventually) be reversed. I think our paper shows that (regardless of what one thinks of cryonics) the irreversibility is the key ethical issue in abortion.

In the end, it will likely take a long time before this is a viable option. But it seems that there are good reasons to consider cryopreservation and reinplantation of fetuses: animal husbandry, space colonisation, various medical treatments (consider “interrupting” an ongoing pregnancy because the mother needs cytostatic treament), and now this family planning reason.

Quantifying busyness

Tempus fugit

If I have one piece of advice to give to people, it is that they typically have way more time now than they will ever have in the future. Do not procrastinate, take chances when you see them – you might never have the time to do it later.

One reason is the gradual speeding up of subjective time as we age: one day is less time for a 40 year old than for a 20 year old, and way less than the eon it is to a 5 year old. Another is that there is a finite risk that opportunities will go away (including our own finite lifespans). The main reason is of course the planning fallacy: since we underestimate how long our tasks will take, our lives tend to crowd up. Accepting to give a paper in several months time is easy, since there seems to be a lot of time to do it in between… which mysteriously disappears until you sit there doing an all-nighter. There is also the likely effect that as you grow in skill, reputation and career there will be more demands on your time. All in all, expect your time to grow in preciousness!

Mining my calendar

I recently noted that my calendar had filled up several weeks in advance, something I think did not happen to this extent a few years back. A sign of a career taking off, worsening time management, or just bad memory? I decided to do some self-quantification using my Google calendar. I exported the calendar as an .ics file and made a simple parser in Matlab.

Histogram of time distance between scheduling time and actual event.

It is pretty clear from a scatter plot that most entries are for the near future – a few days or weeks ahead. Looking at a histogram shows that most are within a month (a few are in the past – I sometimes use my calendar to note when I have done something like an interview that I may want to remember later).

Log-log plot of the histogram of event scheduling intervals.

Plotting it as a log-log diagram suggests it is lighter-tailed than a power-law: there is a characteristic scale. And there are a few wobbles suggesting 1-week, 2-week and 3-week periodicities.

Mean and median distance to newly scheduled events (top), annual number of events scheduled (bottom). The eventual 2015 annual number has been estimated (dashed line).

Am I getting busier? Plotting the mean and median distance to scheduled events, and the number of events per year, suggests yes. The median distance to the things I schedule seems to be creeping downwards, while the number of events per year has clearly doubled from about 400 in 2008 to 800 in 2014 (and extrapolating 2015 suggests about 1000 scheduled events).

Number of calendar events per 14 day period. Red line marks present.

Plotting the number of events I had per 14-day period also suggests that I have way more going on now than a few years ago. The peaks are getting higher and the mean period is more intense.

When am I free?

A good measure of busyness would be the time horizon: how far ahead should you ask me for a meeting if you want to have a high chance of getting it?

One approach would be to look for the probability $Q(t)$ that a day $t$ days ahead is entirely empty. If the probability that I will fill in something $i$ days ahead is $P(i)$ , then the chance for an empty day is $Q(t) = \prod_{i=t}^\infty (1-P(i))$ . We can estimate $P(i)$ by doing a curve-fit (a second degree curve works well), but we can of course just estimate from the histogram counts: $\hat{P}(i)=N(i)/N$ .

Probability that I will have an entirely free day a certain number of days ahead.

However, this method is slightly wrong. Some days are free, others have many different events. If I schedule twice as many events the chance of a free day should be lower. A better way of estimating $Q(t)$ is to think in terms of the rate of scheduling. We can view this as a Poisson process, where the rate of scheduling $\lambda(i)$ tells us how often I schedule something $i$ days ahead. An approximation is $\hat{\lambda}(i)=N(i)/T$ , where $T$ is the time interval we base our estimate on. This way $Q(t) = \prod_{i=t}^\infty e^{-\lambda(i)}$ .

Probability that I will be free a certain number of days ahead for different years of my calender, estimated using a Poisson rate model.

If we slice the data by year, then there seems to be a fairly clear trend towards the planning horizon growing – I have more and more events far into future, and I have more to do. Oh, those halcyon days in 2007 when I presumably just lazed around…

Distance to first day where I have 50%, 75% or 90% chance of being entirely unscheduled.

If we plot when I have 50%, 75% and 90% chance of being free, the trend is even clearer. At present you need to ask about three weeks in advance to have a 50% chance of grabbing me, and 187 days in advance to be 90% certain (if you want an entire working week with 50% chance, this is close to where you should go). Back in 2008 the 50% point was about a week and the 90% point 1.5 months ahead. I have become around 3 times busier.

Conclusions

So, I have become busier. This is of course no evidence of getting more done – a lot of events are pointless meetings, and who knows if I am doing anything helpful at the other events. Plus, I might actually be wasting my time doing statistics and blogging instead of working.

But the exercise shows that it is possible to automatically estimate necessary planning horizons. Maybe we should add this to calendar apps to help scheduling: my contact page or virtual secretary might give you an automatically updated estimate of how far ahead you need to schedule things to have a good chance of getting me. It doesn’t have to tell you my detailed schedule (in principle one could do a privacy attack on the schedule by asking for very specific dates and seeing if they were blocked).

We can also use this method to look at levels of busyness across organisations. Who have flexibility in their schedules, who are so overloaded that they cannot be effectively involved in projects? In the past, tasks tended to be simple and the issue was just the amount of time people had. But today we work individually yet as part of teams, and coordination (meetings, seminars, lectures) are the key links: figuring out how to schedule them right is important for effectivity.

If team member $j$ has scheduling rates $\lambda_j(i)$ and they are are uncorrelated (yeah, right), then $Q(t)=\prod_{i=t}^\infty e^{-\sum_j\lambda_j(i)}$ . The most important lesson is that the chance of everybody being able to make it to any given meeting day declines exponentially with the number of people. If the $\lambda_j(i)$ decline exponentially with time (plausible in at least my case) then scheduling a meeting requires the time ahead to be proportional to the number of people involved: double the meeting size, at least double the planning horizon. So if you want nimble meetings, make them tiny.

In the end, I prefer to live by the advice my German teacher Ulla Landvik once gave me, glancing at the school clock: “I see we have 30 seconds left of the lesson. Let’s do this excercise – we have plenty of time!” Time not only flies, it can be stretched too.

Addendum 2015-05-01

Some further explorations.

Days until next completely free day as a function of time. Grey shows data day-by-day, blue averaged over 7 days, green 30 days and red one year.

Owen Cotton-Barratt pointed out that another measure of busyness might be the distance to the next free day. Plotting it shows a very bursty pattern, with noisy peaks. The mean time was about 2-3 days: even though a lot of time the horizon is far away, often an empty day slips through too. It is just that it cannot be relied on.

Histogram of the timing of events by weekday.

Are there periodicities? The most obvious is the weekly dynamics: Thursdays are busiest, weekend least busy. I tend to do scheduling in a roughly similar manner, with Tuesdays as the top scheduling day.

Number of events scheduled per day, plotted across my calendar.

Over the years, plotting the number of events per day (“event intensity”) it is also clear that there is a loose pattern. Back in 2008-2011 one can see a lower rate around day 75 – that is the break between Hilary and Trinity term here in Oxford. There is another trough around day 200-250, the summer break and the time before the Michaelmas term. However, this is getting filled up over time.

Periodogram of event intensity, showing periodicities in my schedule. Note the weekly and yearly peaks.

Making a periodogram produces an obvious peak for 7 days, and a loose yearly periodicity. Between them there is a bunch of harmonics. The funny thing is that the week periodicity is very strong but hard to see in the map above.

Crispy embryos

Researchers at Sun Yat-sen University in Guangzhou have edited the germline genome of human embryos (paper). They used the ever more popular CRISPR/Cas9 method to try to modify the gene involved in beta-thalassaemia in non-viable leftover embryos from a fertility clinic.

As usual there is a fair bit of handwringing, especially since there was a recent call for a moratorium on this kind of thing from one set of researchers, and a more liberal (yet cautious) response from another set. As noted by ethicists, many of the ethical concerns are actually somewhat confused.

That germline engineering can have unpredictable consequences for future generations is as true for normal reproduction. More strongly, somebody making the case that (say) race mixing should be hindred because of unknown future effects would be condemned as a racist: we have overarching reasons to allow people live and procreate freely that morally overrule worries about their genetic endowment – even if there actually were genetic issues (as far as I know all branches of the human family are equally interfertile, but this might just be a historical contingency). For a possible future effect to matter morally it needs to be pretty serious and we need to have some real reason to think it is more likely to happen because of the actions we take now. A vague unease or a mere possibility is not enough.

However, the paper actually gives a pretty good argument for why we should not try this method in humans. They found that the efficiency of the repair was about 50%, but more worryingly that there was off-target mutations and that a similar gene was accidentally modified. These are good reasons not to try it. Not unexpected, but very helpful in that we can actually make informed decisions both about whether to use it (clearly not until the problems have been fixed) and what needs to be investigated (how can it be done well? why does it work worse here than advertised?).

The interesting thing with the paper is that the fairly negative results which would reduce interest in human germline changes is anyway hailed as being unethical. It is hard to make this claim stick, unless one buys into the view that germline changes of human embryos is intrinsically bad. The embryos could not develop into persons and would have been discarded from the fertility clinic, so there was no possible future person being harmed (if one thinks fertilized but non-viable embryos deserve moral protection one has other big problems). The main fear seems to be that if the technology is demonstrated many others will follow, but an early negative result would seem to reduce this slippery slope argument.

I think the real reason people think there is an ethical problem is the association of germline engineering with “designer babies”, and the conditioning that designer babies are wrong. But they can’t be wrong for no reason: there has to be an ethics argument for their badness. There is no shortage of such arguments in the literature, ranging from ideas of the natural order, human dignity, accepting the given, the importance of an open-ended life to issues of equality, just to mention a few. But none of these are widely accepted as slam-dunk arguments that conclusively show designer babies are wrong: each of them also have vigorous criticisms. One can believe one or more of them to be true, but it would be rather premature to claim that settles the debate. And even then, most of these designer baby arguments are irrelevant for the case at hand.

All in all, it was a useful result that probably will reduce both risky and pointless research and focus on what matters. I think that makes it quite ethical.

The end of the worlds

George Dvorsky has a piece on Io9 about ways we could wreck the solar system, where he cites me in a few places. This is mostly for fun, but I think it links to an important existential risk issue: what conceivable threats have big enough spatial reach to threaten a interplanetary or even star-faring civilization?

This matters, since most existential risks we worry about today (like nuclear war, bioweapons, global ecological/societal crashes) only affect one planet. But if existential risk is the answer to the Fermi question, then the peril has to strike reliably. If it is one of the local ones it has to strike early: a multi-planet civilization is largely immune to the local risks. It will not just be distributed, but it will almost by necessity have fairly self-sufficient habitats that could act as seeds for a new civilization if they survive. Since it is entirely conceivable that we could have invented rockets and spaceflight long before discovering anything odd about uranium or how genetics work it seems unlikely that any of these local risks are “it”. That means that the risks have to be spatially bigger (or, of course, that xrisk is not the answer to the Fermi question).

Of the risks mentioned by George physics disasters are intriguing, since they might irradiate solar systems efficiently. But the reliability of them being triggered before interstellar spread seems problematic. Stellar engineering, stellification and orbit manipulation may be issues, but they hardly happen early – lots of time to escape. Warp drives and wormholes are also likely late activities, and do not seem to be reliable as extinctors. These are all still relatively localized: while able to irradiate a largish volume, they are not fine-tuned to cause damage and does not follow fleeing people. Dangers from self-replicating or self-improving machines seems to be a plausible, spatially unbound risk that could pursue (but also problematic for the Fermi question since now the machines are the aliens). Attracting malevolent aliens may actually be a relevant risk: assuming von Neumann probes one can set up global warning systems or “police probes” that maintain whatever rules the original programmers desire, and it is not too hard to imagine ruthless or uncaring systems that could enforce the great silence. Since early civilizations have the chance to spread to enormous volumes given a certain level of technology, this might matter more than one might a priori believe.

So, in the end, it seems that anything releasing a dangerous energy effect will only affect a fixed volume. If it has energy $E$ and one can survive it below a deposited energy $e$ , if it just radiates in all directions the safe range is $r = \sqrt{E/4 \pi e} \propto \sqrt{E}$ – one needs to get into supernova ranges to sterilize interstellar volumes. If it is directional the range goes up, but smaller volumes are affected: if a fraction $f$ of the sky is affected, the range increases as $\propto \sqrt{1/f}$ but the total volume affected scales as $\propto f\sqrt{1/f}=\sqrt{f}$ .

Self-sustaining effects are worse, but they need to cross space: if their space range is smaller than interplanetary distances they may destroy a planet but not anything more. For example, a black hole merely absorbs a planet or star (releasing a nasty energy blast) but does not continue sucking up stuff. Vacuum decay on the other hand has indefinite range in space and moves at lightspeed. Accidental self-replication is unlikely to be spaceworthy unless is starts among space-moving machinery; here deliberate design is a more serious problem.

The speed of threat spread also matters. If it is fast enough no escape is possible. However, many of the replicating threats will have sublight speed and could hence be escaped by sufficiently paranoid aliens. The issue here is if lightweight and hence faster replicators can always outrun larger aliens; given the accelerating expansion of the universe it might be possible to outrun them by being early enough, but our calculations do suggest that the margins look very slim.

The more information you have about a target, the better you can in general harm it. If you have no information, merely randomizing it with enough energy/entropy is the only option (and if you have no information of where it is, you need to radiate in all directions). As you learn more, you can focus resources to make more harm per unit expended, up to the extreme limits of solving the optimization problem of finding the informational/environmental inputs that cause desired harm (=hacking). This suggests that mindless threats will nearly always have shorter range and smaller harms than threats designed by (or constituted by) intelligent minds.

In the end, the most likely type of actual civilization-ending threat for an interplanetary civilization looks like it needs to be self-replicating/self-sustaining, able to spread through space, and have at least a tropism towards escaping entities. The smarter, the more effective it can be. This includes both nasty AI and replicators, but also predecessor civilizations that have infrastructure in place. Civilizations cannot be expected to reliably do foolish things with planetary orbits or risky physics.

[Addendum: Charles Stross has written an interesting essay on the risk of griefers as a threat explanation. ]

[Addendum II: Robin Hanson has a response to the rest of us, where he outlines another nasty scenario. ]

Do we want the enhanced military?

Some notes on Practical Ethics inspired by Jonathan D. Moreno’s excellent recent talk.

My basic argument is that enhancing the capabilities of military forces (or any other form of state power) is risky if the probability that they can be misused (or the amount of expected/maximal damage in such cases) does not decrease more strongly. This would likely correspond to some form of moral enhancement, but even the morally enhanced army may act in a bad manner because the values guiding it or the state commanding it are bad: moral enhancement as we normally think about it is all about coordination, the ability to act according to given values and to reflect on these values. But since moral enhancement itself is agnostic about the right values those values will be provided by the state or society. So we need to ensure that states/societies have good values, and that they are able to make their forces implement them. A malicious or stupid head commanding a genius army is truly dangerous. As is tails wagging dogs, or keeping the head unaware (in the name of national security) of what is going on.

In other news: an eclipse in a teacup:

Consequentialist world improvement

I just rediscovered an old response to the Extropians List that might be worth reposting. Slight edits.

On 06/10/2012 16:17, Tomaz Kristan wrote:

>> If you want to reduce death tolls, focus on self-driving cars.
> Instead of answering terror attacks, just mend you cars?

Sounds eminently sensible. Charlie makes a good point: if we want to make the world better, it might be worth prioritizing fixing the stuff that makes it worse according to the damage it actually makes. Toby Ord and me have been chatting quite a bit about this.

Death

In terms of death (~57 million people per year), the big causes are cardiovascular disease (29%), infectious and parasitic diseases (23%) and cancer (12%). At least the first and last are to a sizeable degree caused or worsened by ageing, which is a massive hidden problem. It has been argued that malnutrition is similarly indirectly involved in 15-60% of the total number of deaths: often not the direct cause, but weakening people so they become vulnerable to other risks. Anything that makes a dent in these saves lives on a scale that is simply staggering; any threat to our ability to treat them (like resistance to antibiotics or anthelmintics) is correspondingly bad.

Unintentional injuries are responsible for 6% of deaths, just behind respiratory diseases 6.5%. Road traffic alone is responsible for 2% of all deaths: even 1% safer cars would save 11,400 lives per year. If everybody reached Swedish safety (2.9 deaths per 100,000 people per year) it would save around 460,000 lives per year – one Antwerp per year.

Now, intentional injuries are responsible for 2.8% of all deaths. Of these suicide is responsible for 1.53% of total death rate, violence 0.98% and war 0.3%. Yes, all wars killed about the same number of people as were killed by meningitis, and slightly more than the people who died of syphilis. In terms of absolute numbers we might be much better off improving antibiotic treatments and suicide hotlines than trying to stop the wars. And terrorism is so small that it doesn’t really show up: even the highest estimates put the median fatalities per year in the low thousands.

So in terms of deaths, fixing (or even denting) ageing, malnutrition, infectious diseases and lifestyle causes is a far more important activity than winning wars or stopping terrorists. Hypertension, tobacco, STDs, alcohol, indoor air pollution and sanitation are all far, far more pressing in terms of saving lives. If we had a choice between ending all wars in the world and fixing indoor air pollution the rational choice would be to fix those smoky stoves: they kill nine times more people.

Existential risk

There is of course more to improving the world than just saving lives. First there is the issue of outbreak distributions: most wars are local and small affairs, but some become global. Same thing for pandemic respiratory disease. We actually do need to worry about them more than their median sizes suggest (and again the influenza totally dominates all wars). Incidentally, the exponent for the power law distribution of terrorism is safely strongly negative at -2.5, so it is less of a problem than ordinary wars with exponent -1.41 (where the expectation diverges: wait long enough and you get a war larger than any stated size).

There are reasons to think that existential risk should be weighed extremely strongly: even a tiny risk that we loose all our future is much worse than many standard risks (since the future could be inconceivably grand and involve very large numbers of people). This has convinced me that fixing the safety of governments needs to be boosted a lot: democides have been larger killers than wars in the 20th century and both seems to have most of the tail risk, especially when you start thinking nukes. It is likely a far more pressing problem than climate change, and quite possibly (depending on how you analyse xrisk weighting) beats disease.

How to analyse xrisk, especially future risks, in this kind of framework is a big part of our ongoing research at FHI.

Happiness

If instead of lives lost we look at the impact on human stress and happiness wars (and violence in general) look worse: they traumatize people, and terrorism by its nature is all about causing terror. But again, they happen to a small set of people. So in terms of happiness it might be more important to make the bulk of people happier. Life satisfaction correlates to 0.7 with health and 0.6 with wealth and basic education. Boost those a bit, and it outweighs the horrors of war.

In fact, when looking at the value of better lives, it looks like an enhancement in life quality might be worth much more than fixing a lot of the deaths discussed above: make everybody’s life 1% better, and it corresponds to more quality adjusted life years than is lost to death every year! So improving our wellbeing might actually matter far, far more than many diseases. Maybe we ought to spend more resources on applied hedonism research than trying to cure Alzheimers.

Morality

The real reason people focus so much about terrorism is of course the moral outrage. Somebody is responsible, people are angry and want revenge. Same thing for wars. And the horror tends to strike certain people: my kind of global calculations might make sense on the global scale, but most of us think that the people suffering the worst have a higher priority. While it might make more utilitarian sense to make everybody 1% happier rather than stop the carnage in Syria, I suspect most people would say morality is on the other side (exactly why is a matter of some interesting ethical debate, of course). Deontologists might think we have moral duties we must implement no matter what the cost. I disagree: burning villages in order to save them doesn’t make sense. It makes sense to risk lives in order to save lives, both directly and indirectly (by reducing future conflicts).

But this requires proportionality: going to war in order to avenge X deaths by causing 10X deaths is not going to be sustainable or moral. The total moral weight of one unjust death might be high, but it is finite. Given the typical civilian causality ratio of 10:1 any war will also almost certainly produce far more collateral unjust deaths than the justified deaths of enemy soldiers: avenging X deaths by killing exactly X enemies will still lead to around 10X unjust deaths. So achieving proportionality is very, very hard (and the Just War Doctrine is broken anyway, according to the war ethicists I talk to). This means that if you want to leave the straightforward utilitarian approach and add some moral/outrage weighting, you risk making the problem far worse by your own account. In many cases it might indeed be the moral thing to turn the other cheek… ideally armoured and barbed with suitable sanctions.

Conclusion

To sum up, this approach of just looking at consequences and ignoring who is who is of course a bit too cold for most people. Most people have Tetlockian sacred values and get very riled up if somebody thinks about cost-effectiveness in terrorism fighting (typical US bugaboo) or development (typical warmhearted donor bugaboo) or healthcare (typical European bugaboo). But if we did, we would make the world a far better place.

Bring on the robot cars and happiness pills!

Andart II

Part of Anders' Exoself

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