Wolfram Alpha : For a Better Search

Wolfram Alpha (also written as WolframAlpha and Wolfram|Alpha) is an answer engine developed by Wolfram Research. It is an online service that answers factual queries directly by computing the answer from structured data, rather than providing a list of documents or web pages that might contain the answer as a search engine might. It was announced in March 2009 by Stephen Wolfram, and was released to the public on May 15, 2009.

A Computational Knowledge Engine for the Web

In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.

It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example. Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What country is Timbuktu in?” or “How many protons are in a hydrogen atom?” or “What is the average rainfall in Seattle?”

Think about that for a minute. It computes the answers. Wolfram Alpha doesn’t simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.

How Does it Work?

Wolfram Alpha is a system for computing the answers to questions. To accomplish this it uses built-in models of fields of knowledge, complete with data and algorithms, that represent real-world knowledge.

For example, it contains formal models of much of what we know about science — massive amounts of data about various physical laws and properties, as well as data about the physical world.Based on this you can ask it scientific questions and it can compute the answers for you. Even if it has not been programmed explicity to answer each question you might ask it.

But science is just one of the domains it knows about — it also knows about technology, geography, weather, cooking, business, travel, people, music, and more.

It also has a natural language interface for asking it questions. This interface allows you to ask questions in plain language, or even in various forms of abbreviated notation, and then provides detailed answers.The vision seems to be to create a system wich can do for formal knowledge (all the formally definable systems, heuristics, algorithms, rules, methods, theorems, and facts in the world) what search engines have done for informal knowledge (all the text and documents in various forms of media).

Building Blocks for Knowledge Computing

Wolfram Alpha is almost more of an engineering accomplishment than a scientific one — Wolfram has broken down the set of factual questions we might ask, and the computational models and data necessary for answering them, into basic building blocks — a kind of basic language for knowledge computing if you will. Then, with these building blocks in hand his system is able to compute with them — to break down questions into the basic building blocks and computations necessary to answer them, and then to actually build up computations and compute the answers on the fly.

Wolfram’s team manually entered, and in some cases automatically pulled in, masses of raw factual data about various fields of knowledge, plus models and algorithms for doing computations with the data. By building all of this in a modular fashion on top of the Mathematica engine, they have built a system that is able to actually do computations over vast data sets representing real-world knowledge. More importantly, it enables anyone to easily construct their own computations — simply by asking questions.

The scientific and philosophical underpinnings of Wolfram Alpha are similar to those of the cellular automata systems he describes in his book, “A New Kind of Science” (NKS). Just as with cellular automata (such as the famous “Game of Life” algorithm that many have seen on screensavers), a set of simple rules and data can be used to generate surprisingly diverse, even lifelike patterns. One of the observations of NKS is that incredibly rich, even unpredictable patterns, can be generated from tiny sets of simple rules and data, when they are applied to their own output over and over again.

In fact, cellular automata, by using just a few simple repetitive rules, can compute anything any computer or computer program can compute, in theory at least. But actually using such systems to build real computers or useful programs (such as Web browsers) has never been practical because they are so low-level it would not be efficient (it would be like trying to build a giant computer, starting from the atomic level).

The simplicity and elegance of cellular automata proves that anything that may be computed — and potentially anything that may exist in nature — can be generated from very simple building blocks and rules that interact locally with one another. There is no top-down control, there is no overarching model. Instead, from a bunch of low-level parts that interact only with other nearby parts, complex global behaviors emerge that, for example, can simulate physical systems such as fluid flow, optics, population dynamics in nature, voting behaviors, and perhaps even the very nature of space-time. This is the main point of the NKS book in fact, and Wolfram draws numerous examples from nature and cellular automata to make his case.

But with all its focus on recombining simple bits of information and simple rules, cellular automata is not a reductionist approach to science — in fact, it is much more focused on synthesizing complex emergent behaviors from simple elements than in reducing complexity back to simple units. The highly synthetic philosophy behind NKS is the paradigm shift at the basis of Wolfram Alpha’s approach too. It is a system that is very much “bottom-up” in orientation.

Wolfram has created a set of building blocks for working with formal knowledge to generate useful computations, and in turn, by putting these computations together you can answer even more sophisticated questions and so on. It’s a system for synthesizing sophisticated computations from simple computations. Of course anyone who understands computer programming will recognize this as the very essence of good software design. But the key is that instead of forcing users to write programs to do this in Mathematica, Wolfram Alpha enables them to simply ask questions in natural language questions and then automatically assembles the programs to compute the answers they need.

This is not to say that Wolfram Alpha IS a cellular automata itself — but rather that it is similarly based on fundamental rules and data that are recombined to form highly sophisticated structures. The knowledge and intelligence it contains are extremely modularized and can be used to synthesize answers to factual questions nobody has asked yet. The questions are broken down to their basic parts and then simple reasoning takes places, and answers are computed on the vast knowledge base in the system. It appears the system can make inferences and do some basic reasoning across what it knows — it is not purely reductionist in that respect; it is generative, it can synthesize new knowledge, if asked to.

Wolfram Alpha perhaps represents what may be a new approach to creating an “intelligent machine” that does away with much of the manual labor of explicitly building top-down expert systems about fields of knowledge (the traditional AI approach, such as that taken by the Cyc project), while simultaneously avoiding the complexities of trying to do anything reasonable with the messy distributed knowledge on the Web (the open-standards Semantic Web approach). It’s simpler than top down AI and easier than the original vision of Semantic Web.

Generally if someone had proposed doing this to me, I would have said it was not practical. But Wolfram seems to have figured out a way to do it. The proof is that he’s done it. It works. I’ve seen it myself.

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USB 3.0 Speeds Up Performance on External Devices

The USB connector has been one of the greatest success stories in the history of computing, with more than 2 billion USB-connected devices sold to date. But in an age of terabyte hard drives, the once-cool throughput of 480 megabits per second that a USB 2.0 device can realistically provide just doesn't cut it any longer.

What is it? USB 3.0 (aka "SuperSpeed USB") promises to increase performance by a factor of 10, pushing the theoretical maximum throughput of the connector all the way up to 4.8 gigabits per second, or processing roughly the equivalent of an entire CD-R disc every second. USB 3.0 devices will use a slightly different connector, but USB 3.0 ports are expected to be backward-compatible with current USB plugs, and vice versa. USB 3.0 should also greatly enhance the power efficiency of USB devices, while increasing the juice (nearly one full amp, up from 0.1 amps) available to them. That means faster charging times for your iPod--and probably even more bizarre USB-connected gear like the toy rocket launchers and beverage coolers that have been festooning people's desks.

When is it coming? The USB 3.0 spec is nearly finished, with consumer gear now predicted to come in 2010. Meanwhile, a host of competing high-speed plugs--DisplayPort, eSATA, and HDMI--will soon become commonplace on PCs, driven largely by the onset of high-def video. Even FireWire is looking at an imminent upgrade of up to 3.2 gbps performance. The port proliferation may make for a baffling landscape on the back of a new PC, but you will at least have plenty of high-performance options for hooking up peripherals.

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Memristor: A Groundbreaking New Circuit

Photograph: Courtesy of HP

Since the dawn of electronics, we've had only three types of circuit components--resistors, inductors, and capacitors. But in 1971, UC Berkeley researcher Leon Chua theorized the possibility of a fourth type of component, one that would be able to measure the flow of electric current: the memristor. Now, just 37 years later, Hewlett-Packard has built one.

What is it? As its name implies, the memristor can "remember" how much current has passed through it. And by alternating the amount of current that passes through it, a memristor can also become a one-element circuit component with unique properties. Most notably, it can save its electronic state even when the current is turned off, making it a great candidate to replace today's flash memory.

Memristors will theoretically be cheaper and far faster than flash memory, and allow far greater memory densities. They could also replace RAM chips as we know them, so that, after you turn off your computer, it will remember exactly what it was doing when you turn it back on, and return to work instantly. This lowering of cost and consolidating of components may lead to affordable, solid-state computers that fit in your pocket and run many times faster than today's PCs.
Someday the memristor could spawn a whole new type of computer, thanks to its ability to remember a range of electrical states rather than the simplistic "on" and "off" states that today's digital processors recognize. By working with a dynamic range of data states in an analog mode, memristor-based computers could be capable of far more complex tasks than just shuttling ones and zeroes around.
When is it coming? Researchers say that no real barrier prevents implementing the memristor in circuitry immediately. But it's up to the business side to push products through to commercial reality. Memristors made to replace flash memory (at a lower cost and lower power consumption) will likely appear first; HP's goal is to offer them by 2012. Beyond that, memristors will likely replace both DRAM and hard disks in the 2014-to-2016 time frame. As for memristor-based analog computers, that step may take 20-plus years.

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Conficker (Downadup) This Time More Intelligent

Conficker, the worm that exploded into prominence in January when it infected millions of machines, exploiting an already-patched bug in Windows that Microsoft had thought dire enough to fix outside its usual update schedule. The worm hijacked a large number of PCs - estimates ranged as high as 12 million at one point - and then assembled them into a massive botnet able to spread malware, plant fake antivirus software or distribute huge amounts of spam.

It all started in late-October of 2008, we began to receive reports of targeted attacks taking advantage of an as-yet unknown vulnerability in Window’s remote procedure call (RPC) service. Microsoft quickly released an out-of-band security patch (MS08-067), going so far as to classify the update as “critical” for some operating systems—the highest designation for a Microsoft Security Bulletin.

It didn’t take long for malware authors to utilize this vulnerability in their malicious code. In early November, W32.Kernelbot.A and W32.Wecorlappeared, demonstrating limited success in exploiting MS08-067. Still much of the talk at the time focused on the potential havoc that could be caused by this vulnerability, as opposed to damage caused by these threats.

It wasn’t until late November that W32.Downadup appeared (also called Conficker by some news agencies and antivirus vendors). This threat achieved modest propagation success, partly due to its borrowing from the work of the Metasploit Project, which had been actively developing more reliable proof-of-concept methods to take advantage of the vulnerability.

This was one thing that set the Downadup worm apart from its counterparts of the last few years its technical versatility. These secondary tricks weren’t new; there were just so many of them. It scanned the network for vulnerable hosts, but didn’t flood it with traffic, selectively querying various computers in an attempt at masking its traffic instead. It attempted to brute force commonly used network passwords. It took advantage of Universal Plug and Play to pass through routers and gateways. And when the network proved too secure, it used a rather clever AutoPlay trick to get users to execute it from removable drives.

The threat even protected itself from takeover. Transferred payload files were encrypted, as well as digitally signed, and only the Downadup authors had the key. A “hot patching” routine for MS08-067 prevented further exploitation by other attackers or threats. The threat’s authors went to great lengths to prevent buffer overflow exploitation of their own code. No one was going to hijack this worm’s network of potential bots.

But the hidden danger behind all of this was the potential payload Downadup contained the ability to update itself or receive additional files for execution. Again, not a new technique, but in this case the threat was generating a list of 250 new domains to connect to every day. Any one of these domains could potentially contain an update that, if downloaded, would allow the threat to perform further malicious actions. What sort of actions? Anything the authors wanted really. Not only that, but the threat contained its own peer-to-peer (P2P) updating mechanism, allowing one infected computer to update another. Blocking access to the domains might protect you from one vector, but blocking a P2P update is a different matter.

Infection rates began to decline in mid-February, as news of the threat spread and network administrators that had not applied MS08-067 scrambled to protect their networks. This could be in part due to the fact that, with propagation success, the threat garnered a significant amount of attention. The domains meant to update the threat were being closely watched by security vendors, researchers, and the media. This attention isn’t surprising given the threat’s complexity and the way it has utilized so many time-tested malicious code tricks, reinventing a few along the way. There is no doubt that Downadup has been the hot topic for 2009.

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