In Depth: 12 net technologies that shaped the decade
It seems like a long time since the year 2000. Much of the tech we take for granted didn't exist at the turn of the century, and yet technologies such as Twitter have become part and parcel of our everyday lives.
Here are the online technologies, sites and services that future historians will see as defining the last decade.
1. Napster
Napster wasn't around for very long - it launched in 1999 and was shuttered in 2001. The arrival of peer-to-peer file sharing of MP3 files blew the music business to smithereens. Napster got popular very fast and was sued by Metallica in April 2000. Had the record industry embraced it rather than fought it, file sharing might not have become the industry-killing concern it is today.
2. BitTorrent
BitTorrent wasn't designed for sharing music and movies: it was designed to speed up downloads. It just happened to be brilliant for sharing music and movies too, hoovering up bandwidth at such a rate that many ISPs now block or throttle it.
3. Facebook
Twitter gets the headlines, but Facebook gets the traffic: the Harvard-only social network expanded to cover other universities, then companies, then the whole world, and it now has over 350 million active users. It's the AOL of the 21st Century, but its ambitions don't stop there: its Facebook Connect acts as an electronic bouncer for a wide range of non-Facebook sites, including Yahoo ones.
4. AJAX
AJAX - Asynchronous JavaScript and XML - enables your web browser to get more data without refreshing the page, and it's the secret to browser-based applications that don't demand the installation of plugins. Google was the first big firm to really spot the potential of AJAX, using it to create services including Gmail and Google Maps and kicking off the web application explosion.
5. Blogger
Blogs were around before Blogger, but it took blogging from a niche pursuit to mainstream activity: Pyra Labs' 1999 invention had amassed hundreds of thousands of users during the early noughties by the time Google came waving its wallet in 2003. Its secret was simple: Blogging with Blogger was - and is - a doddle.
6. Twitter
In truth, Twitter is less popular than Facebook - but the speed at which Facebook is attempting to copy it shows how influential it's already become. Twitter's genius is twofold: you can follow people without getting their permission, which means a cat may look at a king, and there's a huge number of Twitter clients you can use to access the service. It's the email of social networking.
7. Flickr
Barely five years old, Flickr has transformed the way we think of digital photography. Tagging makes exploring images easier, group pools enable users to collaborate on anything from art to breaking news, and unlimited uploads means Flickr Pro accounts are among the few things online worth paying for. Yahoo bought up the site in 2005.
8. Wikipedia
Wikipedia is an extraordinary achievement, a compendium of knowledge that cost nothing to create and costs nothing to access. Are there errors? Of course there are - just as there are in printed encyclopaedias. Try fixing errors in those overnight. Wikipedia is awesome, and it's one of the single best things about the Internet.
9. PayPal
While many virtual banks appeared and disappeared over the decade, PayPal has gone from strength to strength. It's not the prettiest or cheapest way to wire money around the world, but it's safe, solid, secure and enormously successful. eBay bought it in a $1.5 billion deal in 2002.
10. YouTube
What did we do before YouTube brought us videos of cats falling off skateboards? YouTube isn't here for that, though: it's a media channel for dissidents in Iran, it's the home of viral marketing, and it's rapidly becoming a broadcaster in its own right. It's also offering increasingly high quality: in recent months we've seen more and more YouTube content in HD, and now it's moving to 1080p HD. Its brightest idea, though, was the Embed button, which enables site owners and bloggers to add YouTube clips to their sites in seconds.
11. Spotify
Could Spotify be the Holy Grail of digital music? It's certainly generated acres of press coverage, but more importantly it seems to be making a dent in file sharing - something the music industry has tried and failed to do for a decade. Spotify is digital music done right, offering decent sound quality, a decent collection of music and, if you go for the premium version, a superb mobile phone client that enables you to take your music with you.
12. Google
Not so much because of search - although of course Google ends the decade as the King of Search - but because of Google's other adventures, from Google Docs and Google Maps to Chrome and the forthcoming Chrome OS. What Microsoft was to the 1990s, Google is to the noughties.
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In Depth: How artificial intelligence mimics the human brain
We've all got a very sophisticated processing unit – the brain – that can perform some remarkable tasks.
Despite their speed and memory capacity, silicon-based computers struggle to emulate it. The branch of computer science called Artificial Intelligence tries to narrow the gap, and one of the basic tools of AI is the neural network. So let's take a look at what the neural network can do.
Over the years, Artificial Intelligence has had its ups and downs. Generally there would be a period of 'up' when, after a short run of successful papers, researchers would start making prognostications about their discipline that would grow ever more fanciful. This would naturally lead to a period of 'down' when these predictions did not come to pass.
However, just as spin-off software from the space program have made their way into retail products, spin-offs from AI are becoming part of our lives through intelligent software, even though we may not recognise it as such.
Keeping it real
One fairly recent example that comes to mind is the ability of some point-and-shoot cameras to detect when a face is in shot and hence focus on that face. The face detection software is remarkably fast and rarely wrong, so when taking portraits with these cameras it's easy to trust that the faces of the subjects will be in focus and exposed correctly.
Apple's new version of its iPhoto app goes one step further: it includes face recognition software. Import your photos into iPhoto, and it will detect faces. It's then able to recognise the same faces in different photos. Once you've 'named' the face, iPhoto will annotate the picture with the faces it recognises.
Another business-oriented application of AI algorithms is voice recognition in programs like Dragon Naturally Speaking and OSes like Windows 7.
Some cars that include optional 'Technology' packages also have voice recognition for controlling the car's interior functions like the radio or the heating. (I've given up talking to my car: since I'm British but living in the States, the car's voice recognition software doesn't 'get' my voice, perhaps because it's optimised for a US accent.)
Yet another example is OCR (Optical Character Recognition). Here the state of play is quite remarkable, with the top-end packages declaring over 99 per cent accuracy for typewritten or typeset text. Even the old Palm Pilot PDAs had very constrained – yet very successful – handwriting recognition software; once you'd trained yourself to write the modified characters, the PDA recognised them as swiftly as you could write them.
Although these AI applications use many different techniques to do their magic, there is a very fundamental building block called the neural network, from which many of these techniques are but refinements.
How it works
Before we can get an appreciation of what a neural network does, we should look at the biological background from which it is derived.
If you looked at a brain in a microscope you'd see that it consists of specialised cells called neurons. Neurons are peculiar cells indeed.
The main body of the neuron is called the soma, and it has a veritable forest of dendrites through which input signals arrive. If the number of incoming signals is sufficient, the difference in voltage potential will cause the axon hillock to fire its own signal down the axon, a comparatively long extension of the cell.
The axon branches out towards the end, and at the end of each branch is a synapse that connects to a dendrite of another neuron. The signal travels through the synapse (we talk of the synapse firing) into the dendrite and this signal then participates in whether the next neuron fires or not.
So, boiling this down to the absolute fundamentals (without worrying about the chemical processes that help the signal travel across the synaptic gap, or about the myriad other processes in the cell) we have:
- a set of input signals coming into the cell from other cells;
- if the sum of the signals reaches a threshold, the cell fires its own signal;
- the output signal from a cell will become the input signal to several other cells.
So, in short: inputs, summation and, if above threshold, output. Sounds computer-like.
In the human brain there are roughly 20 billion neurons (the number depends on various factors, including age and gender). Each neuron will be connected through synapses to roughly 10,000 other neurons.
The brain is a giant, complicated network of dendritic connections. Unlike computers, it's massively parallel: computations are going on all over the brain. It boggles the mind how complex it is – indeed, how it works at all.
So let's draw back from the brink and look at how we might mimic this in computing.
Replicating nature
Sadly, there's no way we can mimic 20 billion neurons with 10,000 connections each, but there are several interesting things we can do with much less firepower.
Way back in 1957, Frank Rosenblatt modelled a single neuron with something he called a 'perceptron', and used it to investigate pattern recognition. Unfortunately, the perceptron was unable to recognise even simple functions like XOR (proved formally by Marvin Minsky and Seymour Papert in 1969) and so it was abandoned in favour of something called multilayer feedforward networks.
Nevertheless we can use many of the concepts associated with the perceptron later on.
Above shows a standard perceptron. We have a set of inputs on the left-hand side. Each input has a 'weight' associated with it. Each input signal (which is a floating-point value, positive or negative) is multiplied by its weight (another floating-point value).
All of these products are summed. If the sum exceeds a threshold value (generally 0), the perceptron outputs 1 (or 'true'). If the threshold is not exceeded, the perceptron outputs 0 (or 'false').
This test is known as the activation function. To help with the process, another fixed input is usually provided (known as the 'bias'). This models the propensity of the perceptron to fire in spite of the values of its inputs. The bias is normally 1 and will have its own weight.
All this is very well, but where do the weights come from? The inputs are obviously provided by us in some form, but who provides the weights?
Look at it like this. Suppose we want a perceptron to calculate the same result as the AND operation. There will be two inputs to this perception, A and B. Each input will be constrained to two possible values, 0 and 1. If both A and B equal 1, the perceptron should output 1; otherwise it should output 0.
We have to determine three weights here: the weights for A and B and the bias. Once we have these, we should be able to run the perceptron, and it should produce the correct outputs for the four possible combinations of input. The only way of doing this is to train the perceptron.
First thing we need is a set of inputs and their expected outputs. For our simple example, we have four training sets: 1 and 1 gives 1, 0 and 1 gives 0, 0 and 0 gives 0, and 1 and 0 gives 0.
We set all weights to zero. Note that this perceptron will produce the right answer for the last three training sets automatically. The first set will produce an error (it should produce 1, but gives 0, an error of 1).
What we do now is to modify the weights to take account of the error. We make use of a new constant called the learning rate (a number between 0 and 1) and modify each weight to add a term that's proportional to the error value, the learning rate and its input value.
Start off with a high rate (say, 0.8). We then let the perceptron learn using its training materials until the weights stabilise. If the weights don't converge after a few iterations, the perceptron is possibly oscillating around the solution, so it's best to reduce the learning rate.
If the weights never converge, then the function being modelled by the perceptron cannot be recognised.
After Minsky and Papert showed that a single perceptron couldn't solve some simple patterns, research stagnated. Eventually, efforts shifted to studying a multilayer system instead.
The first such system was called the feedforward neural network. In a multilayer system of perceptrons, there are at least three layers: the input layer, the hidden layer and the output layer. The latter two layers are the perceptrons. Below shows an idealised view of such a network.
Notice that the data or signals travel one way, from the input layer to the output layer, hence the term feedforward. There are no cycles here. The hidden layer is shown here to have three perceptrons, but this is by no means a fixed number.
Indeed, the number of hidden perceptrons is yet another 'knob' to twiddle to tune the neural network (the weights being the only knobs so far). The number of output perceptrons is a function of the pattern you're trying to recognise, so if you were trying to perform OCR on digits, you might have 10 output perceptrons, one for each digit.
Notice that all of the input signals feed into all of the perceptrons in the hidden layer, and all the outputs from the perceptrons in the hidden layer feed into the perceptrons in the output layer. If an input for a perceptron is not needed, the weight will be set to zero.
Finer tuning
The big issue with this neural network is in training it. The most successful algorithm devised is known as the back-propagation training algorithm, but it requires some changes.
The first change is that the perceptron should output not just a 0 or a 1, but a floating-point value between 0 and 1. This will in turn require the perceptron to use a different activation function, one that's a curve instead of being a step function.
The functions used here are known as sigmoid functions. These are S-shaped functions with asymptotes at 0 and at 1.
Below shows the standard one that's used: f(x) = 1/(1+e-x), but others include the hyperbolic tangent (tanh) or the error function (erf).
If a perceptron calculates a very large sum of its weighted inputs, it'll output a value close to 1; if the sum is very large and negative, it'll output a value close to 0; if it's close to 0, the perceptron will output a value around 0.5.
Once these changes have been made, the network is 'differentiable'; that is, it's possible to calculate gradients. The gradients we want to calculate are to help us change the weights due to a training error: a steep gradient for an input signal means a larger change in its weight, a more gradual gradient means a smaller change.
The gradient also gives a direction (upwards or downwards), so we know whether to add or subtract the correction term. And all this means that, despite the much greater complexity of a feedforward neural network, training it still only requires a few cycles.
Obviously you have to pre-calculate a catalogue of training sets (so, for example, if you were creating a neural network to recognise the digits using OCR, you'd use as many different variants of the digits using all the fonts you could find), and those training sets would be fed into the network as often as needed until the weights converged.
Of course, this time around, you would have the extra knob to twiddle: the number of hidden perceptrons. Here there are no real guidelines apart from the more of them there are, the longer it will take to train the network, and you may not gain any more accuracy.
Generally, though, you would aim for having at least as many hidden perceptrons as you have perceptrons in the output layer.
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Ford to turn new cars into moving Wi-Fi zones
Ford is set to offer its new car customers the option of turning their vehicle into a moving Wi-Fi hotspot in 2010.
The next gen Sync in-car entertainment system will use a USB mobile broadband modem to establish a secure Wi-Fi connection.
The system will be available in 2010 on selected Ford cars and will not require users to take any additional subscriptions to mobile internet services – users will merely use their own USB Wi-Fi routers to get online. The Wi-Fi enabled models are still to be confirmed by Ford.
Shop and socialise in the car
"While you're driving to grandma's house, your spouse can be finishing the holiday shopping and the kids can be chatting with friends and updating their Facebook profiles," said Mark Fields, Ford president of the Americas.
"And you're not paying for yet another mobile subscription or piece of hardware because Ford will let you use technology you already have."
Ford claims interest in in-car connectivity is increasing rapidly, with one-third of people surveyed by the Consumer Electronics Association in the US wanting the option of being able to get online while in the car.
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In Depth: The Mac switcher's guide to running Windows alongside OS X
Even though you'll come to love OS X, you might still want access to Windows. Maybe you need programs that you just can't get for the Mac; you may want to use PC software that you already own; or you might simply like the security blanket of flicking back to Windows.
There are two ways to run Windows on a Mac. The first is with a utility called Boot Camp, which is included with any new Mac. This installs Windows, either by splitting your Mac's single hard disk in two, or – if you have a Mac Pro with more than one drive – by installing Windows on a second internal disk.
It's free (apart from the cost of a Windows licence if you don't have one, or don't want to transfer it from your old PC). It also uses your Mac's hardware to the full – if you're a keen PC gamer, for example, you can reboot into Windows to get the best from your Mac's graphics card.
The downside is that you have to shut down the Mac and reboot into Windows. Once there, you can't run any of your Mac applications (though happily, with Mac OS X 10.6 you can at least access the files on your Mac partition). This is why we prefer virtualisation.
This second option uses a piece of inexpensive Mac software – VMware Fusion and Parallels Desktop for Mac are the best – to run Windows at the same time as your Mac. There are different ways of doing this.
You can constrain the Windows virtual computer to a window in Mac OS X; run it full screen; or choose a mode (Fusion calls it Unity, Parallels opts for Coherence) that 'cuts the Windows windows out' and puts them alongside your Mac ones.
Using virtualisation, you have access to files on the Mac and PC at the same time, and can drag and drop between them. You can even open a document on the Mac with an application running on your Windows virtual machine.
Best of all, you don't have to choose between Boot Camp and virtualisation. Both Fusion and Parallels can use your Boot Camp partition as the source for their virtual machine. This way, you can boot into Windows for performance, or run it side-by-side with Mac OS X if convenience is more important.
How to configure Boot Camp
1. Launch Boot Camp Assistant (Applications > Utilities) and print the installation guide. Decide how much space to allocate to Windows. If you choose 32GB or smaller, you can format the drive as FAT, and be able to write to it from the Mac.
2. Once you've rebooted into the Windows installer, pick the target partition.
For XP, use C: Partition3
Run the installer's tools to format the partition as NTFS or FAT.
3. You'll now go through the process of creating a user. Don't set a complex password, as exotic characters won't type properly until the new 'PC' knows what a Mac keyboard looks like.
Reinsert your Mac OS X 10.5 or 10.6 disc for the necessary drivers.
How to use VMware Fusion
1. First, insert your Windows installer CD and wait for it to appear on your Mac desktop. When you launch Fusion, it can look at this mounted CD to work out what system you're trying to install; it will then help you pick what settings are most appropriate.
2. You're asked if you want to do an Easy Install. With this option, Fusion picks appropriate settings based on which operating system you're installing. It then automates the rest of the install for you; just punch in your serial number.
3. After the new operating system in your virtual machine has loaded, you have to install VMware Tools, a suite of drivers and utilities. If you chose Easy Install, this happens automatically, otherwise pick the option from the Virtual Machine menu.
How to use Parallels Desktop for Mac
1. Parallels can detect what kind of operating system you're trying to install, so ensure that the disc is inserted before you begin. To install from a disc image – for example if you have a Linux build on an ISO – specify it with the second radio button.
2. If you use the Easy Install option, just enter a username and Windows product key and everything is automated. In this screen, you can choose a few more options as Windows installs, including putting a shortcut icon on your Desktop – very handy.
3. If you ran Easy Install, Parallels loads the helper drivers automatically, otherwise you have to do so yourself. By default, it activates Coherence mode, which hides the Windows desktop and blends it into your Mac. It also offers antivirus software.
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Ferrari working on hybrid gas-electric supercar
Ferrari's long-rumoured gas-electric hybrid supercar could well be arriving as early as this coming spring, according to latest reports.
The news comes from Italy's Quattroroute magazine, which claims that Maranello is building a hybrid based on the 588 GTB Fiorano
Sexing-up hybrids
Ferrari's new hybrid car is rumoured to be on show at the Geneva auto show this coming March, with a reported 35 percent overall improvement in fuel economy.
New gas-electric cars from Ferrari can only help to make 'hybrid' motoring tech more aspirational and desirable - which is altogether a good thing.
Autocar also recently reported that Ferrari is planning a hybrid drivetrain for the successor to the 612 Scaglietti, in order to improve handling, and due for launch in 2014.
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How to ethically recycle unwanted tech and presents online
One of the eternal problems with Christmas is the problem of what to do with all of those unwanted Christmas presents from various aunties, uncles and distant relatives.
No more. Now you can quickly and easily and ethically recycle all of your unwanted gifts with a few clicks of the mouse button, while none of your gift-givers are looking (ideally!).
Games, tech, jumpers
If you have a bunch of Blu-rays, games, DVDs or CDs that you don't want, you can sign up to PlayTrade, to convert all of those unwanted discs into cash.
If you are lucky enough to have been given a new computer or laptop this Christmas, and you are feeling particularly generous, then you can head over to Computer Aid's site to find out more about how to ethically recycle old computing equipment in a way that will directly benefit school-kids in developing countries.
Finally, if you just wish to get shot of that awful jumper that your nan bought you (again) then you can head over to register with JumbleAID to give it to somebody who will at least appreciate it a little more than you and give some cash to a charity of your choice at the same time.
So instead of feeling like a guilty and worthless ingrate, you can bask in the knowledge that you have recycled all those pressies in a way that has brought some happiness to someone perhaps a little more in need of them that you.
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In Depth: 15 game-changing Linux moments of the decade
If you were sat at your Linux computer one dark evening in late 1999, things would have been considerably different.
Your machine would probably be running either Red Hat 6.1 or Mandrake 6.
Outside your window, the world was going crazy for all things dotcom. Microsoft was prepping both Windows 2000 and its ill-fated Millennium edition, while Apple had just released OS 9 and its Power Mac G4.
As a Linux user, you'd have been an uber-geek, someone with an obsessive interest in computing and far too much time on your hands.
But things have changed. Linux is now an operating system anyone can install and use, and it's growing stronger every year. Here's how it happened.
January 2001: Linux Kernel 2.4
The kernel is the only part of the operating system officially called 'Linux'. It all started with, and is still maintained by, Linus Torvalds.
Version 2.4 was a watershed, bringing Linux support to many essential interfaces, starting with USB, and eventually Bluetooth, RAID and the ext3 filesystem. Linux's amazing driver support started here, and the current 2.6 revision owes many of its advances to the 2.4 release.
May 2001: Nvidia releases binary drivers
Until May 2001, many desktop Linux users were isolated when it came to official hardware vendor support. But then Nvidia released a binary version of its graphics driver.
This gave us massive improvement in 3D performance. But it also opened a can of worms, as the debates on the legality of closed drivers linked to the kernel, and whether we should use them, still continue to this day.
DRIVING LINUX: Nvidia is one of the few companies who provides drivers that offer similar performance to their Windows and OS X counterparts
June 2002: Gnome 2.0
Gnome was trying to catch up with the wayward and revolutionary KDE 2.0 desktop, released 10 months earlier. Regardless of which desktop was best, both versions offered a revolutionary experience when compared to the staid, static and ascetic desktops of the previous generation.
The Gnome project has gone from strength to strength after the release of 2.0, and great things are promised for 3.0 in 2010.
May 2002: OpenOffice.org 1.0
Few would consider using Linux if there wasn't the semblance of Microsoft Office compatibility. Sun Microsystems bought, renamed and released its own broadly compatible office suite for free, in what it must have hoped would be a flanking attack on Microsoft's dominance. A tactic it revisited with the re-licence of Java in 2007.
March 2003: SCO's lawsuit against IBM
For a long time, this lawsuit cast a long shadow over Linux adoption. SCO alleged that IBM had allowed parts of its UNIX operating system to be re-licenced and subsequently used within Linux.
If IBM lost, Linux would need to be modified. At one point, even the GPL was under scrutiny when SCO claimed it was unconstitutional. But as yet, litigation has come to nothing and SCO is currently fighting against bankruptcy.
April 2004: X.org 1.0
With free software, if you don't like what other people are doing you can take their work and build your own version. This is what happened with XFree86, the technology at the heart of display, mouse and keyboard interaction. When its licence changed, many Linux distributions were forced to find an alternative, and this became X.org.
October 2004: Ubuntu Warty Warthog
It's the only Linux distribution in our list, but whether you love or hate Ubuntu, there's no denying that its appearance on the scene has changed Linux dramatically.
For mainstream media, it's now often a byword for Linux, and thanks to its charismatic astronaut leader, Linux has a free software advocate to compete with Steve Jobs and Steve Ballmer.
BIG UP UBUNTU: Ubuntu releases are named after the year and month of their release. Warty's official title was Ubuntu 4.10
November 2004: Firefox 1.0
Firefox works on many operating systems other than Linux, but it was the first free software application to be understood and adopted by the general public on this scale. It was faster, more secure, and had more features than its proprietary competitors, changing the popular perception of open source.
ON FIRE: A two-page ad in the New York Times accompanied the release of Firefox 1.0, featuring the names of thousands of contributors to the fund-raising campaign
April 2005: Mandrake becomes Mandriva
Mandrake Linux was the Ubuntu of its day. It was the distribution that made Linux feasible to use for many of us. But its continued decline can be traced to this point, where Mandrake lost its community focus, eventually sacked its founder and became marginalised and overtaken by smaller, more dynamic distributions.
January 2006: First release of Compiz
Desktop cubes, wobbly windows and drop shadows all started when David Reveman came back from weeks in isolation and announced both Xgl and Compiz, the technologies that have gone on to transform eye-candy on the Linux desktop.
It's thanks to Compiz that Linux has been able to keep abreast of developments on both Windows and OS X.
EYE CANDY: Desktop cubes may be a little old-hat now, but in 2006 it was a jaw-dropping effect
June 2007: GPLv3
The third release of the one licence to rule them all hasn't been as smooth as many people had hoped. Linus, a critic in the drafting stage, has so far declined to port the Linux kernel to the new version, but there is growing momentum.
Version 3 takes a much harder stand against closed online use of open source software, and embedded systems that don't give the user full access to the systems. But it is finally gaining acceptance.
January 2008: KDE 4.0
The jump from KDE 2 to 3 was never this troubled. Even today, version 4 feels only half finished to many people, almost two years after the initial release. But it's also a release crammed full of innovation, including pervasive widgets, the removal of the desktop metaphor and the semantic desktop that will blur local and online content.
WIDGET IT: KDE 4 bravely attempts to turn everything on your desktop into a widget
February 2008: The Asus EeePC
It may have started with an initiative to provide one laptop per child, but netbooks have become one of the most important technologies of today for the future.
The Asus EeePC has been surpassed by other devices, but Linux is keeping abreast of development, thanks to Intel's Moblin project, and more recently, Google's Chrome OS announcement.
September 2008: Android 1.0 SDK released
Google would be a different company if it had to buy a licence for every machine running in its Linux server farms. And in 2008, it took a similar strategy when it announced it was entering the mobile phone market.
Android has gone on to become a stable platform, and a great example of what Linux is capable of in the hands of a company like Google.
April 2009: Oracle buys Sun (MySQL)
The decade ends with the sun setting on one of the most influential Unix and open source companies to survive the 90s. Custodian of Java, VirtualBox, MySQL and OpenOffice.org, along with major contributions to Gnome, Mozilla and the Linux kernel itself, the effects of Sun's acquisition are yet to be realised. But whatever happens to Sun, these projects are safeguarded, thanks to the open source licences they use.
SAFE SUN: Linux can freely take from any of Sun's open source projects, including its OpenSolaris operating system
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Will dark matter neutralinos top trump the god particle?
All talk (at least around the physics lab water-cooler) may have been about the Higgs Boson – or the God particle to give it its more dramatic moniker – but the humble neutralino could potentially steal its thunder in the coming year, according to scientists.
With the Large Hadron Collider now active and continuing on its path to enlighten/destroy humanity, the Higgs Boson could now potentially be spotted for the first time in the tenties/teenies.
However, according to New Scientist, the as-yet unseen neutralino could be ready to steal in from stage left and elbow its way onto the front pages.
Supersymmetry
For those that aren't sure what the neutralino is (tssk), we can inform you that it is a key part of the theory of supersymmetry – where every elementary particle has a super-partner.
Perhaps more headline grabbing is the suggestion that neutralino could well be what is generally known as dark matter, and that it could be properly discovered outside of theory in the coming 12 months.
We wait with baited breath.
Via New Scientists
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In Depth: 10 best iPhone apps for music fans
Apple's iPhone and iPod touch are in many ways evolved iPods.
Since the iPod rapidly became king of the music players, it's fitting that many great apps for music fans are available on the App Store, to push your device beyond the limitations of the iPod app and your own music collection.
1. Spotify (free; requires آ£9.99 per month subscription)
The App Store has about 50 billion one-star reviews for Spotify, written by people who seem to think it's daylight robbery that you should have to pay for music.
Although we'd sooner see the fee drop to a fiver a month, Spotify for iPhone nonetheless remains a joy. You can play over 3G, access your Spotify playlists, and sync on-the-fly between desktop and mobile versions. Even better, Spotify for iPhone caches up to 3,333 tracks for up to 30 days.
2. Last.fm (free)
Although Last.fm has been overshadowed by Spotify, the iPhone app's worth a download, due to it creating personalised radio stations based on your favourite artists or genres.
You might not get to hear precisely what you want, but chances are you'll discover new music that appeals.
3. Internet Radio Box (آ£0.59)
If you can't be bothered splashing out on a DAB radio, plonk your iPhone into a dock, connect its audio output to some speakers, and play one of over 30,000 radio stations through Internet Radio Box.
Shake-to-shuffle is a nice touch, but all the important features are there, too, including favourites management, a configurable sleep timer, and the ability to play in locked mode.
4. Simplify Music 2 (آ£3.49)
If you're annoyed at your device's meagre capacity and long for a 200GB iPhone, Simplify Music is the next best thing.
In tandem with a Simplify account, an app on your Mac or PC, and a Wi-Fi or 3G connection, you can access your (non-DRM) music from anywhere, along with streamed music from collections of up to 30 friends.
5. Lyrics+ (آ£1.19)
Coming across like a plug-in for iPod, Lyrics+ enables you to show lyrics while you play your favourite songs. Most of the iPod app's functionality is present and correct, enabling easy access to your music. Although not all lyrics are available, the developer notes that those shown are legal, drawn from LyricFind, and lyrics previously viewed are cached for offline usage.
6. Shazam (free/آ£2.99)
The theory behind Shazam is simple: hit Tag Now and have it listen to and analyse whatever music's playing around you.
The app then attempts to tell you what the song is, saving you later embarrassingly trying to sing it to your friends. In practice, Shazam is hit and miss, but when it works, the app sends a shiver down your spine, due to its spookily accurate nature.
7. Adaptunes (آ£0.59)
Utilising your device's advanced innards for tracking motion, Adaptunes adjusts your device's volume, making it louder as you speed up in your car.
This saves you fiddling around with your device when you come off of local roads and start belting along the motorway.
8. PlaySafe (آ£0.59)
Another in-car app, PlaySafe turns your device's screen into a giant button, enabling you to prod it to play/pause, or swipe to skip tracks - all while keeping your eye on the road.
It has shortcomings - notably forcing in-app playlist creation rather than directly integrating with the iPod app - but it's worth a look if your car lacks physical iPod controls.
9. Remix David Bowie—Space Oddity (آ£1.19)
Despite the over-excited nature of this app's info page - "you ARE Ground Control!" - Remix David Bowie shows how modern handhelds can provide fans with more than just an MP3 and some art.
Here, for a price a little more than the song itself, you get a multitrack that you can play with and randomise with a shake.
10. nin: access (free)
Although it initially fell foul of over-zealous App Store reviewers, nin: access finally made it into the wider world in July 2009.
The app provides a portal into the world of Nine Inch Nails, offering news, photos, streaming audio, and the ability to swap messages and photos with local fans.
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