Ars Technica on Felten‘s appointment as FTC’s first Chief Technologist:

Princeton computer science professor Ed Felten today was tapped for a one-year stint at the FTC in a decision so shockingly sane that it’s still a bit hard to believe.

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Twitter for Grown-Ups

Derek Powazek has some pretty good advice on how to use Twitter like a reasonable person. Gina Trapani likes to follow everyone important (to be polite), but only actually reads tweets from a scant 9% of those people who are actually interesting.

I’ve tried this method before, but I don’t like it. It’s a hassle to maintain, and it just doesn’t feel “right”. The bottom line is that Twitter isn’t Facebook. Don’t follow people on Twitter because they’re your friends; follow them because they say things that interest you. If you don’t want to read what they’re saying, don’t follow them. If I’m following you on twitter it’s because I enjoy your posts; that’s it.

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A is for Apocalypse

A’s the apocalypse, a dry, barren waste
B is for bombs, misfired in haste
C is for cyborgs, cold and unfeeling
D is the Doctor, with tales sending you reeling
E is “exterminate!”, a Dalek’s shrill cry
F is for forbidden fruits you’re denied
G is for gods, dead things of the past
without our worship, how long could they last?
H is for hopes: oh how ours were wrong
I is the id, and the monsters there-from
J is for jumping to hyperspace speeds
K is for Kirk with his alien needs
L is for luminous, a collection of tales
M is for mind control, when all else fails
N is for nothing: that which we most fear
O is for probes saying ‘we’re over here’
P is for punk, and the steam that goes with it
powering airships of steel and old rivets
Q is for asking the questions we fear
and hoping that answers to them don’t appear
R is for replicants, not wanting to die
S is something we make: strong ai
T is for telomeres, thwarting our cloning
U is the universe (something we’re alone in)
V is for vampires: a savage, cold breed,
bleeding us dry for our blood (which they need)
W is a werewolf, which howls in the nigh,
spreading the curse to each creature it bites
X is for xenomorphs and for xenologers
Y is for you: fighting for all of us
Z is for zombies, all rotten and pale
things to roam over the earth if we fail

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Real Cybersecurity Begins at Home

Loose Tweets Sink Fleets by Brian Lane Winfied on Flickr

The Tech Liberation Front has a great article rejecting some of the thoughtless panic around the way that Google’s streetview cars have been mapping wifi networks. One of their subtitles really jumped out at me:

Real Cyber Security Begins at Home

This totally captures my thoughts on the matter. If you’re going to be transmitting over the air in plaintext, and broadcasting into a public place, you should be neither surprised nor appalled if (or when) someone else picks up that communication, whether deliberately, or by accident. Don’t whine about it: take affirmative steps to protect yourself.

However, what I really want to see are some posters in the style of WWII propaganda:

  • Cybersecurity Begins at Home: Secure your Wifi
  • Cybersecurity Begins at Home: Use HTTPs on the Web
  • Cybersecurity Begins at Home: Encrypt your Hard-Drive

Sounds like a good project for the EFF.

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I do not enjoy fireworks. I already know how dangerous and destructive men can be, without being reminded how beautiful we find destruction. I prefer fireflies: silent, natural, and safe.

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The trial of the Pirate Bay came into public awareness in early-to-mid 2008, when the site’s administrators were charged with promoting copyright infringement. Even though the entire trial was centered around the evidence that 21 music files, nine movies and four games had been pirated by the site’s users, the prosecution claimed $13 million in damages. Now, this is a website that links to thousands upon thousands of torrents every single day for the past five years. If a total of 34 downloaded files were valued at $13 million, then the Pirate Bay must be the most successful organized crime syndicate ever run in the history of the entire world.

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Caffeine not as Effective as Previously Thought

A cup of coffee

Coffee Time by ルーク.チャン.チャン on Flickr

The Register reports on yesterday’s advance online publication of a study in Nature‘s Journal of Neuropsychopharmacology. The study, which has the snappy title of:

Association of the Anxiogenic and Alerting Effects of Caffeine with ADORA2A and ADORA1 Polymorphisms and Habitual Level of Caffeine Consumption

investigates the effects of coffee consumption among certain groups. They were looking at what coffee does to improve alertness or cause anxiety. The study investigates various phenomena, primarily how certain genes influence coffee’s effects. However, the truly interesting results come when they look at coffee’s effects on those who regularly do / do not use caffeine. From the abstract:

Placebo administration in participants decreased alertness and increased headache. Caffeine did not increase alertness in [none/low caffeine user] participants. With frequent consumption, substantial tolerance develops to the anxiogenic effect of caffeine, even in genetically susceptible individuals, but no net benefit for alertness is gained, as caffeine abstinence reduces alertness and consumption merely returns it to baseline.

That’s right: this study found that caffeine has no value as a performance-enhancing drug. If you are not a regular user of caffeine, caffeine will not make you more alert; in fact it will simply make you anxious. If you are a regular user of caffeine, you’re simply an addict: caffeine will return your alertness to a normal level, but no higher, and only because your alertness decreases when you haven’t used caffeine in a while. On the plus side, regular caffeine users have a high tolerance for the anxiousness-inducing side-effect.

Bottom line: if you think that tea or coffee will make you think sharper, this study disagrees.

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Writing Law Like We Write Code

A old typewriter languishing in a field of amber grass.

Writer's Block by thorinside on Flickr

As I, and others, have remarked before: laws bear certain resemblances to computer code. A state’s constitution is like the operating system for that country. We have different types of government: democracy, monarchy, &c, just as we have different operating systems. Even among a particular system, each state has its particular version, with its own customs, forms, and processes, reflecting its unique politics, geography and people. Similarly, everyone’s computer is slightly different, with different settings, themes, and so on. Individual statutes are like software, specially written for that system, and customised to its individual needs.

Like computer code, legal code needs to work, and it must be written carefully to ensure that it does. First comes the idea, whether it’s a law prohibiting flag-folding, or a piece of software for managing your gardening. Then someone writes a first draft. Whether this is a congressional aid composing legalese, or a geek writing C, the process is broadly similar. After the first draft, however the processes diverge sharply.

A computer programmer can test their code by running it. The writer of the gardening-management app can try it out. They can run it on different hardware, and see how it interacts with other software. They can test the user interface: push all the buttons, and see that they have the desired effect. If something isn’t quite right, they can tweak it, trying different options, to see what works. Perhaps the user-interface is functional, but counter-intuitive. The software designer can test a number of alternatives, and see how people respond.

Laws do not have that same luxury. Society is – surprising as it may sound – far more complex than a computer system. Perhaps the flag-folding law will simply cause people to roll their flags up instead. Perhaps it will cause people to get rid of their flags, because they can no longer store them. Perhaps these are desired effects, perhaps not. Either way, the results are unpredictable, and hard to anticipate: it is very difficult to conduct a beta-test for a law.

Perhaps we should use better techniques to write law? Ed Felten thinks that writers of literature may have something to learn from the way that code is written. He’s referring to the tools and processes that coders use: version control systems, and iterated release cycles. I think that legislators can learn even more from coders: unlike authors, they are writing prose which is functionally a form of code. Computer scientists have developed robust principles for writing good code: do it this way, and your code will be easier to write, and easier to maintain. It’ll work better, and when it breaks, it’ll be easier to fix.

Computer scientists are fundamentally lazy people. They want to do as little work as possible, outsourcing as much as possible to tools. We write modular code, define robust data types, and use standard libaries, so that we only have to implement things once. Once you have a piece of software for flobbing widgets, whenever you’re writing some software that needs its widgets flobbed, call on that module, or library. We like clear interfaces, like APIs, and SDKs, so that we can focus on the logic of the code, rather than on working out how to talk to that other piece of software, or wondering what sort of order it wants lists in.

Legislators have a greater need for these approaches that coders do. Coders use them for efficiency, however, it is quite possible to develop code “the hard way”: it just requires constant testing, to make sure that the code does what it’s meant to do. Legislators, of course, do not have this option, but they still write legal code flatly and natively. It’s incredibly hard to check laws for bugs, errors, and vulnerabilities. As with software, especially given society’s complexity, it may take a very long time before bugs make themselves known, so they’ll be extremely difficult to correct. Anything that can be done to make laws easier to write, parse, and debug will reduce the incidence of bugs, greatly improving the law.

Legislators need all the help that they can get to write laws well the first time. Coders have discovered a number of techniques to help. Legislators should be listening.

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Convince the Convincers

Geek-tastic glasses! by SteffanyF! on Flickr

Audrey Watters recently wrote a piece for ReadWriteWeb, looking at Diaspora, and questioning the general value of including technical details in a product pitch. Her position is immediately comprehensible: a typical user or investor simply isn’t interested in most of the technical details. In her words:

Investors and customers are probably not all that interested in, for example, the intricacies of how you plan to use JSON to handle your payload. They need to know that your product works and works well, and just as importantly, you need to show them why they’d want to use it.

On one level, she’s absolutely correct. Many of the interested parties don’t have the technical chops for such technical jargon to be particularly meaningful. They probably won’t read the technical descriptions; if they do, the technical information will be neither useful nor persuasive. However, it is important to include the geeky bits. In fact, the geeky bits play a crucial role in persuading even lay users.

Non technical folks may not get the geeky bits, but they’re not fools. They understand that there’s something important to the technical specs. Most of them have surely used enough systems that just don’t quite work to know that technical features can make or break a product. However, they also know that they can’t parse the technical info. What they can do, is read the opinions of technical folks that they trust. If the tech specs are up to scratch, the technically-minded are likely to comment on it. Part of the buzz for Diaspora comes from geeks, squints, and quants backing up it’s free, open and secure anti-facebook credentials.

That’s how tech specs persuade the non-technical. Incomprehensible tech-speak is parsed by techs, and converted into human readable conclusions, commentary, and recommendations. Non-geeks read their favourite geek blogs, and evaluate the spread of opinions on a project or product. By the time they read the project’s pitch, they don’t need to attempt to decode the jargon: its already been translated for them.

This doesn’t mean that jargon-only pitches are a good idea, far from it. A better approach is to present varying levels of geekery to your readers, and let them choose what they want – like the Creative Commons does with its licenses. Each license has a simple, human-readable front page. Those with the appropriate skills, time, and curiosity can click through and see the legalese in all its fear-inspiring glory.

The same idea works for other projects and products: provide a human-readable abstract, but let the geeks poke around in the technical descriptions, warts and all. Who knows, maybe they can see a problem you missed, or fix a flaw that was stumping you. In any case, the principle is the same: include the technical details. They may not convince all your readers, but they will convince the convincers.

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Glitch Trading?

A graph illustrating the movements of the Dow Jones Industrial Average on May 6th, 2010

Tim Maly of Quiet Babylon, has a thoughtful post about the way that the press deals with a financial market in which most trading is automated. Maly criticises the press for attributing unexpected market behaviour to human responses to news. He notes that 60% of trading is automated, suggesting therefore that software glitches are a far more reasonable explanation for such unexpected events. Consistent with some of his commenters, I disagree with this conclusion.

Just because machines make 60% of the trades doesn’t mean that they constitute 60% of the information coming into the system. In fact, as Maly rightly point out, battlebots (as they are called) do not do a natural language parsing of the news: they respond only to the state of the system itself. This means that the entire information input of the system is the human traders. The reason for these perturbations is the intersection of human traders who respond to news, and machines which respond to the human traders.

The machines may not be well-behaved. Their programmers have given them complex instructions, and the instructions of different machines may not be particularly different. It’s quite possible that some particular – apparently benign – stimulus might cause several machines to react in very similar ways. It’s possible that this group of initial actors may be very large subset of the machines. Moreover, other machines, and human traders which don’t respond directly to that stimulus, may respond similarly to the synchronised activity of the initial actors.

The market is a very complex system. It is very difficult to instruct a machine to act sensibly under all possible circumstances. The machine’s software will is designed under some particular model of the market’s behaviour. Whatever model is used, there is always the possibility of black swan events, which are unaccounted for under this model. Even with an incredibly sophisticated model, the machine’s software may not be a rational optimiser under all cases. Even if the behaviour of one machine may be somewhat predictable, the emergent behaviour of a large group of machines – like that of a flock of birds, or a school of fish – may be as difficult to predict as that of the market itself.

This synchronised emergent behaviour may result in poor collective action. Given that these events are quite possibly outside the model used to create the machines’ software, there may even be non-optimal individual action. If this is what Maly means by a “software glitch”, then we have a quibble of language, and perhaps causality, but little more than that. When I hear “software glitch”, I think of a rounding error, buffer overflow, or similar low-level problem, not a problem with the high-level logic of the model which the machine uses. After all, this sort of logic error is not really much different when acted on by a machine than by a human. Machines are faster, and less-likely to second-guess their decisions, but their decisions are likely to be much the same.

Whatever happens, some small set of human traders (constituting no more than 40% of trades) read the news, and take some action. We may not know exactly how their minds work; we may not even consider their actions to be particularly odd. Whatever their decisions are, they create some sort of mild change or perturbation in the market. A large group of machines faithfully and dutifully execute their well-programmed, but incompletely-reasoned instructions. This causes a significant market shift, amplified by more traders and machines acting rationally according to their models. None of this is caused by a rounding error, or a fat finger: just a series of actors duly going by their playbooks.

However, the initial impetus comes from the human traders. Their actions are the only ones influenced by news. That influence may be complex and tenuous. Human psychology may be as complex as trading software, if not more so. The simple explanations reported by the press may be speculative fiction designed to sell copy. Nonetheless, their core assumption is sound. People make trades in response to information. Only after that do the machines exhibit complex, unpredictable, emergent behaviour which – depending on the stimulus – may yield aberrant results.

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