What is the Singularity? | Singularity Institute for Artificial Intelligence

August 2nd, 2010 § 0

Overview

What is the Singularity?

The Singularity is the technological creation of smarter-than-human intelligence. There are several technologies that are often mentioned as heading in this direction. The most commonly mentioned is probably Artificial Intelligence, but there are others: direct brain-computer interfaces, biological augmentation of the brain, genetic engineering, ultra-high-resolution scans of the brain followed by computer emulation. Some of these technologies seem likely to arrive much earlier than the others, but there are nonetheless several independent technologies all heading in the direction of the Singularity – several different technologies which, if they reached a threshold level of sophistication, would enable the creation of smarter-than-human intelligence.

A future that contains smarter-than-human minds is genuinely different in a way that goes beyond the usual visions of a future filled with bigger and better gadgets. Vernor Vinge originally coined the term “Singularity” in observing that, just as our model of physics breaks down when it tries to model the singularity at the center of a black hole, our model of the world breaks down when it tries to model a future that contains entities smarter than human.

Human intelligence is the foundation of human technology; all technology is ultimately the product of intelligence. If technology can turn around and enhance intelligence, this closes the loop, creating a positive feedback effect. Smarter minds will be more effective at building still smarter minds. This loop appears most clearly in the example of an Artificial Intelligence improving its own source code, but it would also arise, albeit initially on a slower timescale, from humans with direct brain-computer interfaces creating the next generation of brain-computer interfaces, or biologically augmented humans working on an Artificial Intelligence project.

Some of the stronger Singularity technologies, such as Artificial Intelligence and brain-computer interfaces, offer the possibility of faster intelligence as well as smarter intelligence. Ultimately, speeding up intelligence is probably comparatively unimportant next to creating better intelligence; nonetheless the potential differences in speed are worth mentioning because they are so huge. Human neurons operate by sending electrochemical signals that propagate at a top speed of 150 meters per second along the fastest neurons. By comparison, the speed of light is 300,000,000 meters per second, two million times greater. Similarly, most human neurons can spike a maximum of 200 times per second; even this may overstate the information-processing capability of neurons, since most modern theories of neural information-processing call for information to be carried by the frequency of the spike train rather than individual signals. By comparison, speeds in modern computer chips are currently at around 2GHz – a ten millionfold difference – and still increasing exponentially. At the very least it should be physically possible to achieve a million-to-one speedup in thinking, at which rate a subjective year would pass in 31 physical seconds. At this rate the entire subjective timespan from Socrates in ancient Greece to modern-day humanity would pass in under twenty-two hours.

Humans also face an upper limit on the size of their brains. The current estimate is that the typical human brain contains something like a hundred billion neurons and a hundred trillion synapses. That’s an enormous amount of sheer brute computational force by comparison with today’s computers – although if we had to write programs that ran on 200Hz CPUs we’d also need massive parallelism to do anything in realtime. However, in the computing industry, benchmarks increase exponentially, typically with a doubling time of one to two years. The original Moore’s Law says that the number of transistors in a given area of silicon doubles every eighteen months; today there is Moore’s Law for chip speeds, Moore’s Law for computer memory, Moore’s Law for disk storage per dollar, Moore’s Law for Internet connectivity, and a dozen other variants.

By contrast, the entire five-million-year evolution of modern humans from primates involved a threefold increase in brain capacity and a sixfold increase in prefrontal cortex. We currently cannot increase our brainpower beyond this; in fact, we gradually lose neurons as we age. (You may have heard that humans only use 10% of their brains. Unfortunately, this is a complete urban legend; not just unsupported, but flatly contradicted by neuroscience.) An Artificial Intelligence would be different. Some discussions of the Singularity suppose that the critical moment in history is not when human-equivalent AI first comes into existence but a few years later when the continued grinding of Moore’s Law produces AI minds twice or four times as fast as human. This ignores the possibility that the first invention of Artificial Intelligence will be followed by the purchase, rental, or less formal absorption of a substantial proportion of all the computing power on the then-current Internet – perhaps hundreds or thousands of times as much computing power as went into the original Artificial Intelligence.

But the real heart of the Singularity is the idea of better intelligence or smarter minds. Humans are not just bigger chimps; we are better chimps. This is the hardest part of the Singularity to discuss – it’s easy to look at a neuron and a transistor and say that one is slow and one is fast, but the mind is harder to understand. Sometimes discussion of the Singularity tends to focus on faster brains or bigger brains because brains are relatively easy to argue about compared to minds; easier to visualize and easier to describe. This doesn’t mean the subject is impossible to discuss; section III of our “Levels of Organization in General Intelligence” does take a stab at discussing some specific design improvements on human intelligence, but that involves a specific theory of intelligence, which we don’t have room to go into here.

However, that smarter minds are harder to discuss than faster brains or bigger brains does not show that smarter minds are harder to build – deeper to ponder, certainly, but not necessarily more intractable as a problem. It may even be that genuine increases in smartness could be achieved just by adding more computing power to the existing human brain – although this is not currently known. What is known is that going from primates to humans did not require exponential increases in brain size or thousandfold improvements in processing speeds. Relative to chimps, humans have threefold larger brains, sixfold larger prefrontal areas, and 98. 4% similar DNA; given that the human genome has 3 billion base pairs, this implies that at most twelve million bytes of extra “software” transforms chimps into humans. And there is no suggestion in our evolutionary history that evolution found it more and more difficult to construct smarter and smarter brains; if anything, hominid evolution has appeared to speed up over time, with shorter intervals between larger developments.

But leave aside for the moment the question of how to build smarter minds, and ask what “smarter-than-human” really means. And as the basic definition of the Singularity points out, this is exactly the point at which our ability to extrapolate breaks down. We don’t know because we’re not that smart. We’re trying to guess what it is to be a better-than-human guesser. Could a gathering of apes have predicted the rise of human intelligence, or understood it if it were explained? For that matter, could the 15th century have predicted the 20th century, let alone the 21st? Nothing has changed in the human brain since the 15th century; if the people of the 15th century could not predict five centuries ahead across constant minds, what makes us think we can outguess genuinely smarter-than-human intelligence?

Because we have a past history of people making failed predictions one century ahead, we’ve learned, culturally, to distrust such predictions – we know that ordinary human progress, given a century in which to work, creates a gap which human predictions cannot cross. We haven’t learned this lesson with respect to genuine improvements in intelligence because the last genuine improvement to intelligence was a hundred thousand years ago. But the rise of modern humanity created a gap enormously larger than the gap between the 15th and 20th century. That improvement in intelligence created the entire milieu of human progress, including all the progress between the 15th and 20th century. It is a gap so large that on the other side we find, not failed predictions, but no predictions at all.

Smarter-than-human intelligence, faster-than-human intelligence, and self-improving intelligence are all interrelated. If you’re smarter that makes it easier to figure out how to build fast brains or improve your own mind. In turn, being able to reshape your own mind isn’t just a way of starting up a slope of recursive self-improvement; having full access to your own source code is, in itself, a kind of smartness that humans don’t have. Self-improvement is far harder than optimizing code; nonetheless, a mind with the ability to rewrite its own source code can potentially make itself faster as well. And faster brains also relate to smarter minds; speeding up a whole mind doesn’t make it smarter, but adding more processing power to the cognitive processes underlying intelligence is a different matter.

But despite the interrelation, the key moment is the rise of smarter-than-human intelligence, rather than recursively self-improving or faster-than-human intelligence, because it’s this that makes the future genuinely unlike the past. That doesn’t take minds a million times faster than human, or improvement after improvement piled up along a steep curve of recursive self-enhancement. One mind significantly beyond the humanly possible level would represent a Singularity. That we are not likely to be dealing with “only one” improvement does not make the impact of one improvement any less.

Combine faster intelligence, smarter intelligence, and recursively self-improving intelligence, and the result is an event so huge that there are no metaphors left. There’s nothing remaining to compare it to.

The Singularity is beyond huge, but it can begin with something small. If one smarter-than-human intelligence exists, that mind will find it easier to create still smarter minds. In this respect the dynamic of the Singularity resembles other cases where small causes can have large effects; toppling the first domino in a chain, starting an avalanche with a pebble, perturbing an upright object balanced on its tip. (Human technological civilization occupies a metastable state in which the Singularity is an attractor; once the system starts to flip over to the new state, the flip accelerates.) All it takes is one technology – Artificial Intelligence, brain-computer interfaces, or perhaps something unforeseen – that advances to the point of creating smarter-than-human minds. That one technological advance is the equivalent of the first self-replicating chemical that gave rise to life on Earth.

For more information, continue with “Why Work Toward the Singularity?

Whoa. I need to read this and digest more carefully and will post my thoughts on this concept later…

DATA DRIVEN JOURNALISM

August 2nd, 2010 § 0

Data-driven journalism: What is there to learn?

24 August, 2010, Amsterdam

“Opening up content and data produced by public bodies will enable new forms of reportage as well as a new generation of services enabling the public to participate in the news making process. New tools to analyse, represent, deliver and give context to public data are beginning to revolutionise the way we understand large and complex issues, from Hans Rosling’s analysis of flu statistics, to the Guardian MP expenses crowdsourcing tool, and to the Afghanistan Election Data project. An ecosystem of open data that anyone can reuse or contribute to will be critical for a new generation of data driven journalism to flourish”

- Jonathan Gray, community coordinator, The Open Knowledge Foundation, UK

Ten, even five years ago, the use of data as a basis for reporting was difficult and costly, requiring IT skills far beyond what is common in media. Databases were used mainly by investigative journalists. Editors and reporters usually relied on information provided by outside sources.

Today there is a notable change. Collections of data are becoming available online, often for free. There is a whole stack of tools to dig into ‘big data‘. Open source tools allow navigation and analysis of large amounts of data rather quickly. There are online applications that allow us to share and visualise data.

Developing the know-how to use the available data more effectively, to understand it, communicate and generate stories based on it, could be a huge opportunity to breathe new life into journalism. Journalists can find new roles as ’sense-makers’ digging deep into data, thus making reporting more socially relevant. If done well, delivering credible information and advice could even generate revenues, opening up new perspectives on business models, aside from subscriptions and advertising.

In this context, the European Journalism Centre in collaboration with the University of Amsterdam organises the first round table on data-driven journalism. The one day event brings together specialists in fields which intersect with data-driven journalism: data mining, data visualisation and multimedia storytelling to discuss the possibilities of this emerging field, examine and understand the needed tools and workflows, and spread the know-how for data-driven journalism. What can we learn from the existing projects? How can we integrate the existing tools in the journalistic workflows? What skills are needed to enter this field? These are just a few of the issues which will be addressed in this event.

The programme includes speakers from: The New York Times (US), The Financial Times (UK), The Times (UK), The University of Amsterdam (Netherlands), The Open University (UK), The Open Knowledge Foundation (UK), Medienkombinat (Germany) Hacks/Hackers (US), OWNI (France) IBM (France), Ultra Knowledge (UK), KB Consulting (Germany). The roundtable is chaired by Mirko Lorenz, DDJ Project leader, EJC and Innovation projects, Deutsche Welle.

To share your suggestions and insights on what is needed for journalists to enter this promising field, please join the data-driven journalism group on the EJC Online Community. To attend the event, please fill in the online application form.

There is no registration fee but the attendees are expected to arrange and cover their own transportation and accommodation.

For more information:
Liliana Bounegru
European Journalism Centre
Email: bounegru@ejc.net
Tel: 31 43 325 40 30

Another trend that can be used for both good and bad. How will the public know it is unbiased? It’s like statistics…

Life Logging

July 30th, 2010 § 0

Came across this article from the New York Times – The Data Driven Life.

Which led me to the term of Life Logging.

The goal of lifelogging: to record and archive all information in one’s life. This includes all text, all visual information, all audio, all media activity, as well as all biological data from sensors on one’s body. The information would be archived for the benefit of the lifelogger, and shared with others in various degrees as controlled by him/her.(Lifelogging, An Inevitablity)

Links of interest:

Terminologies

June 22nd, 2009 § 0

During my Thesis, one of the biggest problems I came across while doing research was terminologies.  There are so many terms as wells as definitions, it made me crazy.  Actually, it still makes me crazy.  People come up with new terms, has their own definitions, and there is no standarad yet.  So my mission, is to come up with a mindmap/chart/table – something visual to put everything in some type of order and how they influence each other. What’s driving me is trying to figure out how they relate to each other.  So first step, is to find what terms are being used and come up with a list of what influences this terminology.  This will be a continuing work in progress.

Here’s the plan: (thus far)

  1. Come up with terminologies
  2. Find out how they came out, who defined, when was this term coined, etc
  3. Influences – what areas does the term encompass and to what degree?

Areas/Influences:

  • Interaction
  • Art/Aesthetic/Design
  • Data/Information
  • Flavors: Art, Science, Computer, Technology, etc

Terms:

  • Information Design
  • Information Archectiture
  • knowledge visualization
  • typography
  • User interface design
  • Ambient infovis
  • Social infovis
  • Visual Design
  • Interactive Design
  • Interactive Media
  • New Media
  • Computer Graphics
  • Information Visualization
  • Information Aesthetic Visualization
  • Human Computer Interaction
  • Data Visualization
  • Scientific Visualization
  • Information Graphics
  • Cartography
  • Graphic Design
  • Digital Art
  • Computer Art

I think this is a good starting point.  Each week, I will post about a definition. If you are interested in helping out, drop a comment!

Lifestreaming

June 22nd, 2009 § 0

Lifestreaming is “an online record of a person’s daily activities, either via direct video feed or via aggregating the person’s online content such as blog posts, social network updates, and online photos.” (Wordspy)

I was reading this post from Flowing Data, and it got me thinking why people are either interested or not interested in social data.

Lifestreaming is something I’ve been following for quite some time now.  Heck, I have one of my own that I have been testing out.  Unfortunately, current lifestreaming applications as well as the one I am using, does not quite meet my needs.

I view lifestreaming as my personal online diary. Therefore, when I aggregate all of my social data into an application, I want ALL of my data (including private/protected entries – for example: a protected Twitter account or a Flickr photo that is only viewable by my friends) to show up in the application.  Which leads to a big issue: data portablity.

Case in point: Can you export all your Facebook activity to something?

There are other things that I want bug me which I won’t get into.  But I do plan on doing something about it…

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