Roman France

May 17, 2009

Recession times

When the toilet in Carol Taddei’s master bathroom began to break down a few months ago, she decided it would be cheaper to buy a new one than pay for repairs. Ever frugal in this dismal economy, Ms. Taddei, a retired paralegal, then took her economizing a step further, figuring she could save even more by installing the new toilet herself.

Initially, things looked good with the flushing and the swishing. That is, until the ceiling collapsed in the room below the new (leaky) toilet. Rushing to get supplies for a repair, Ms. Taddei clipped a pole in her garage. It ripped the bumper off her car, and later, several shelves holding flower pots and garden tools collapsed over her head.

“It just kept getting worse,” Ms. Taddei said, ruefully describing what came out to be a $3,000, three-day renovation at her suburban Minneapolis home, finished by a professional from Mr. Handyman, a home repair service that takes emergency calls.

ACUMEN

Etymology

Latin acumen, sharp point

n.

acumen (plural acumens)

1. quickness of discernment or perception; penetration of mind; the faculty of nice discrimination

Quotations

• Sir Arthur Conan Doyle in The Adventure of the Missing Three-Quarter

  No, no, my dear Watson! With all respect for your natural acumen, I do not think that you are quite a match for the worthy doctor.

  
  

• 1991, Silence Of The Lambs

  Hannibal Lecter: Why do you think he removes their skins, Agent Starling? Enthrall me with your acumen.

Synonyms

Sharpness; penetration; keenness; shrewdness; acuteness; acuity.

NOUS

Origin:
1670–80; Gk. noûs, contracted var. of nóos mind

n.

1. Philosophy
   1. Reason and knowledge as opposed to sense perception.
   2. The rational part of the individual human soul.
   3. The principle of the cosmic mind or soul responsible for the rational order of the cosmos.
   4. In Neo-Platonism, the image of the absolute good, containing the cosmos of intelligible beings.
   
   
2. Chiefly British Good sense; shrewdness."She has great social nous"

[Greek.]

Spanish: nos,

German: uns selbst,

Japanese: 私たち自身を

The Grid, Our Cars and the Net: One Idea to Link Them All

The Grid, Our Cars and the Net: One Idea to Link Them All | Autopia

Autopia Planes, Trains, Automobiles and the Future of Transportation

By David Weinberger
May 8, 2009
11:57 am

Editor's note: Robin Chase thinks a lot about transportation and the internet, and how to link them. She connected them when she founded Zipcar, and she wants to do it again by making our electric grid and our cars smarter. Time magazine recently named her one of the 100 most influential people of the year. David Weinberger sat down with Chase to discuss her idea.

Robin Chase considers the future of electricity, the future of cars and the internet three terms in a single equation, even if most of us don't yet realize they're on the same chalkboard. Solve the equation correctly, she says, and we create a greener future where innovation thrives. Get it wrong, and our grandchildren will curse our names.

Chase thinks big, and she's got the cred to back it up. She created an improbable network of automobiles called Zipcar. Getting it off the ground required not only buying a fleet of cars, but convincing cities to dedicate precious parking spaces to them. It was a crazy idea, and it worked. Zipcar now has 6,000 cars and 250,000 users in 50 towns.

Now she's moving on to the bigger challenge of integrating a smart grid with our cars – and then everything else. The kicker is how they come together. You can sum it up as a Tweet: The intelligent network we need for electricity can also turn cars into nodes. Interoperability is a multiplier. Get it right!

Robin Chase

Chase starts by explaining the smart grid. There's broad consensus that our electrical system should do more than carry electricity. It should carry information. That would allow a more intelligent, and efficient, use of power.

"Our electric infrastructure is designed for the rare peak of usage," Chase says. "That's expensive and wasteful."

Changing that requires a smart grid. What we have is a dumb one. We ask for electricity and the grid provides it, no questions asked. A smart grid asks questions and answers them. It makes the meter on your wall a sensor that links you to a network that knows how much power you're using, when you're using it and how to reduce your energy needs – and costs.

Such a system will grow more important as we become energy producers, not just consumers. Electric vehicles and plug-in hybrids will return power to the grid. Rooftop solar panels and backyard wind turbines will, at times, produce more energy than we can store. A smart grid generates what we need and lets us use what we generate. That's why the Obama Administration allocated $4.5 billion in the stimulus bill for smart grid R&D.

This pleases Chase, but it also makes her nervous. The smart grid must be an information network, but we have a tradition of getting such things wrong. Chase is among those trying to convince the government that the safest and most robust network will use open internet protocols and standards. For once the government seems inclined to listen.

Chase switches gears to talk about how cars fit into the equation. She sees automobiles as just another network device, one that, like the smart grid, should be open and net-based.

"Cars are network nodes," she says. "They have GPS and Bluetooth and toll-both transponders, and we're all on our cell phones and lots of cars have OnStar support services."

That's five networks. Automakers and academics will bring us more. They're working on smart cars that will communicate with us, with one another and with the road. How will those cars connect to the network? That's the third part of Chase's equation: Mesh networking.

In a typical Wi-Fi network, there's one router and a relatively small number of devices using it as a gateway to the internet. In a mesh network, every device is also a router. Bring in a new mesh device and it automatically links to any other mesh devices within radio range. It is an example of what internet architect David Reed calls "cooperative gain" - the more devices, the more bandwidth across the network. Chase offers an analogy to explain it.

"Wi-Fi is like a bridge that connects the highways on either side of the stream," she says. "You build it wide enough to handle the maximum traffic you expect. If too much comes, it gets congested. When not enough arrives, you've got excess capacity. Mesh takes a different approach: Each person who wants to cross throws in a flat rock that's above the water line. The more people who do that, the more ways there are to get across the river."

Cooperative gain means more users bring more capacity, not less. It's always right-sized. Of course, Chase points out, if you're trying to go a long distance, you're ultimately forced back onto the broadband bridge where the capacity is limited. But for local intra-mesh access, it's a brilliant and counter-intuitive strategy.

Mesh networking as a broad-based approach to networking is growing. A mesh network with 240 nodes covers Vienna. Similar projects are underway in Barcelona, Athens, the Czech Republic and, before long, in two areas of Boston not far from the cafe we're sitting in. But the most dramatic examples are the battlefields of Iraq and Afghanistan.

"Today in Iraq and Afghanistan, soldiers and tanks and airplanes are running around using mesh networks," said Chase. "It works, it's secure, it's robust. If a node or device disappears, the network just reroutes the data."

And, perhaps most important, it's in motion. That's what allows Chase's plural visions to go singular. Build a smart electrical grid that uses Internet protocols and puts a mesh network device in every structure that has an electric meter. Sweep out the half dozen networks in our cars and replace them with an open, Internet-based platform. Add a mesh router. A nationwide mesh cloud will form, linking vehicles that can connect with one another and with the rest of the network. It's cooperative gain gone national, gone mobile, gone open.

Chase's mesh vision draws some skepticism. Some say it won't scale up. The fact it's is being used in places like Afghanistan and Vienna indicates it could. Others say moving vehicles may not be able to hook into and out of mesh networks quickly enough. Chase argues it's already possible to do so in less than a second, and that time will only come down. But even if every car and every electric meter were meshed, there's still a lot of highway out there that wouldn't be served, right? Chase has an answer for that, too.

"Cars would have cellular and Wi-Fi as backups," she said.

The economics are right, she argues. Rather than over-building to handle peak demand and letting capacity go unused, we would right-size our infrastructure to provide exactly what we need, when we need it, with minimum waste and maximum efficiency.

"There's an economy of network scale here," she says. "The traffic-light guys should be interested in this for their own purposes, and so should the power-grid folks and the emergency responders and the Homeland Security folks and, well, everyone. Mesh networks based on open standards are economically justifiable for any one of these things. Put them together - network the networks – and for the same exact infrastructure spend, you get a ubiquitous, robust, resilient, open communication platform — ripe for innovation — without spending a dollar more."

The time is right, too. There's $7.2 billion in the stimulus bill for broadband, $4.5 billion for the smart grid and about $5 billion for transportation technology. The Transportation Reauthorization bill is coming up, too. At $300 billion it is second only to education when it comes to federal discretionary spending. We are about to make a huge investment in a set of networks. It will be difficult to gather the political and economic will to change them once they are deployed.

"We need to get this right, right now," Chase says.

Build each of these infrastructures using open networking standards and we enable cooperative gain at the network level itself. Get it wrong and we will have paved over a generational opportunity.

David Weinberger is a fellow at Harvard's Berkman Center for Internet and Society. E-mail him at self@evident.com.

Que c'est triste Venise

Que c'est triste Venise
Au temps des amours mortes
Que c'est triste Venise
Quand on ne s'aime plus

On cherche encore des mots
Mais l'ennui les emporte
On voudrais bien pleurer
Mais on ne le peut plus

Que c'est triste Venise
Lorsque les barcarolles
Ne viennent souligner
Que des silences creux

Et que le cœur se serre
En voyant les gondoles
Abriter le bonheur
Des couples amoureux

Que c'est triste Venise
Au temps des amours mortes
Que c'est triste Venise
Quand on ne s'aime plus

Les musées, les églises
Ouvrent en vain leurs portes
Inutile beauté
Devant nos yeux déçus

Que c'est triste Venise
Le soir sur la lagune
Quand on cherche une main
Que l'on ne vous tend pas

Et que l'on ironise
Devant le clair de lune
Pour tenter d'oublier
Ce qu'on ne se dit pas

Adieu tout les pigeons
Qui nous ont fait escorte
Adieu Pont des Soupirs
Adieu rêves perdus

C'est trop triste Venise
Au temps des amours mortes
C'est trop triste Venise
Quand on ne s'aime plus

Bucintoro

2008 February « Venice from beyond the bridge

BUCINTORO

The Bucintoro was the Doge's big parade boat. It was used the Ascension day, when a gold ring where dropped in to the sea as sign of the Republic power over the sea (Sposalizio del mare).

The fist Bucintoro was build by the Republic in 1311, since then it was rebuilt 3 times. It was 35 meters long, 7 meters large and 9 meters high, with 42 oars and 168 oarsmen. The last one was destroyed by the French in 1789.

Now there is a foundation that is trying to rebuilt it, they are looking sponsors for 15.000.000,00 euro.

Wolfram on Wolfram Alfa

Wolfram|Alpha Is Coming!

March 5, 2009

Stephen Wolfram

"Some might say that Mathematica and A New Kind of Science are ambitious projects.

But in recent years I’ve been hard at work on a still more ambitious project—called Wolfram|Alpha.

And I’m excited to say that in just two months it’s going to be going live:

Mathematica has been a great success in very broadly handling all kinds of formal technical systems and knowledge.

But what about everything else? What about all other systematic knowledge? All the methods and models, and data, that exists?

Fifty years ago, when computers were young, people assumed that they’d quickly be able to handle all these kinds of things and that one would be able to ask a computer any factual question, and have it compute the answer.

But it didn’t work out that way. Computers have been able to do many remarkable and unexpected things. But not that.

I’d always thought, though, that eventually it should be possible. And a few years ago, I realized that I was finally in a position to try to do it.

I had two crucial ingredients: Mathematica and NKS. With Mathematica, I had a symbolic language to represent anything—as well as the algorithmic power to do any kind of computation. And with NKS, I had a paradigm for understanding how all sorts of complexity could arise from simple rules.

But what about all the actual knowledge that we as humans have accumulated?

A lot of it is now on the web—in billions of pages of text. And with search engines, we can very efficiently search for specific terms and phrases in that text.

But we can’t compute from that. And in effect, we can only answer questions that have been literally asked before. We can look things up, but we can’t figure anything new out.

So how can we deal with that? Well, some people have thought the way forward must be to somehow automatically understand the natural language that exists on the web. Perhaps getting the web semantically tagged to make that easier.

But armed with Mathematica and NKS I realized there’s another way: explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable.

It’s not easy to do this. Every different kind of method and model—and data—has its own special features and character. But with a mixture of Mathematica and NKS automation, and a lot of human experts, I’m happy to say that we’ve gotten a very long way.

How can I say it?

"But, OK. Let’s say we succeed in creating a system that knows a lot, and can figure a lot out. How can we interact with it?

The way humans normally communicate is through natural language. And when one’s dealing with the whole spectrum of knowledge, I think that’s the only realistic option for communicating with computers too.

Of course, getting computers to deal with natural language has turned out to be incredibly difficult. And for example we’re still very far away from having computers systematically understand large volumes of natural language text on the web.

But if one’s already made knowledge computable, one doesn’t need to do that kind of natural language understanding.

All one needs to be able to do is to take questions people ask in natural language, and represent them in a precise form that fits into the computations one can do.

Of course, even that has never been done in any generality. And it’s made more difficult by the fact that one doesn’t just want to handle a language like English: one also wants to be able to handle all the shorthand notations that people in every possible field use.

I wasn’t at all sure it was going to work. But I’m happy to say that with a mixture of many clever algorithms and heuristics, lots of linguistic discovery and linguistic curation, and what probably amount to some serious theoretical breakthroughs, we’re actually managing to make it work.

Neverending trillions

"Pulling all of this together to create a true computational knowledge engine is a very difficult task.

It’s certainly the most complex project I’ve ever undertaken. Involving far more kinds of expertise—and more moving parts—than I’ve ever had to assemble before.

And—like Mathematica, or NKS—the project will never be finished.

But I’m happy to say that we’ve almost reached the point where we feel we can expose the first part of it.

It’s going to be a website: www.wolframalpha.com. With one simple input field that gives access to a huge system, with trillions of pieces of curated data and millions of lines of algorithms.

We’re all working very hard right now to get Wolfram|Alpha ready to go live.

I think it’s going to be pretty exciting. A new paradigm for using computers and the web.

That almost gets us to what people thought computers would be able to do 50 years ago!

Due Soon: Wolfram Alpha

Wolfram Alpha is an answer-engine developed by the international company Wolfram Research. The service will be an online computational data engine based on intuitive query parsing, a large library of algorithms, and A New Kind of Science approach to answering queries.^[1] It was announced in March 2009 by British physicist Stephen Wolfram, to be launched in May 2009.

Wolfram Alpha differs from search engines in that it does not simply return a list of results based on a keyword, but instead computes answers and relevant visualizations from a collection of known information. Other new search engines, known collectively as semantic search engines, have developed alpha applications of this type, which index a large amount of answers, and then try to match the question to one. Examples of companies using this strategy include True Knowledge, and Microsoft's Powerset.

Wolfram Alpha has many parallels with Cyc, a project aimed at developing a common-sense inference engine since the 80s, though without producing any major commercial application. Cyc founder Douglas Lenat was one of the few given an opportunity to test Wolfram Alpha before its release:

It handles a much wider range of queries than Cyc, but much narrower than Google; it understands some of what it is displaying as an answer, but only some of it ... The bottom line is that there are a large range of queries it can't parse, and a large range of parsable queries it can't answer
-Douglas Lenat^[2]

Wolfram's earlier flagship product Mathematica encompasses computer algebra, numerical computation, visualization and statistics capabilities and can be used on all kinds of mathematical analysis, from simple plotting to signal processing, but will not be included in the alpha release, due to computation-time problems.^[3]

From Positivism to Complexity to Paradoxes

Today's key features of logical positivism (or logical empiricism; see also constructive empiricism), as originally created by A. Comte (19th century) and later adapted and corrected by Karl Popper, are:

1. A focus on science as a product, a linguistic or numerical set of statements;

2. A concern with axiomatization, that is, with demonstrating the logical structure and coherence of these statements (Göedel's 1921 and 1951 demonstrations of the essential insufficiency of many axiomatic systems, have largely reshaped and structured this vision);

3. An insistence on at least some of these statements being testable, that is amenable to being verified, confirmed, or falsified by the empirical observation of reality; statements that would, by their nature, be regarded as untestable included the teleological; (Thus positivism rejects much of classical metaphysics.)

4. The belief that science is markedly cumulative;

5. The belief that science is predominantly transcultural;

6. The belief that science rests on specific results that are dissociated from the personality and social position of the investigator;

7. The belief that science contains theories or research traditions that are largely commensurable;

8. The belief that science sometimes incorporates new ideas that are discontinuous from old ones;

9. The belief that science involves the idea of the unity of science, that there is, underlying the various scientific disciplines, basically one science about one real world;

10. The belief that "all true knowledge is scientific"^[14];

11. The belief that all things are ultimately measurable;

12. The belief that "entities of one kind... are reducible to entities of another,"^[14] such as societies to numbers, or mental events to chemical events (reductionism).

What's new

Major progress over this picture came, at the end of 20th century, from the science (i.e. mathematics) of complexity. It is now clear that the scaling, up or down, of a phenomenum usually produces new laws, that essentially account for new, qualitatively different, phenomena. This essencially challenges the 12th point, above.

In this sense, although macro-processes can, indeed, be "reducible to physiological, physical or chemical events,"^[14] and "social processes are reducible to relationships between and actions of individuals,"^[14] or "biological organisms are reducible to physical systems"^[14] . It is no longer believed that ALL laws of the former phenomena can be tracked back or inferred up, from the later. In a parallel to Goedel's finding, about the incompleteness of most axiomatic mathematical systems, there is now a perception of an essencial insufficiency of micro laws, to explain macro phenomena.

Simple programs, for instance, are capable of a remarkable range of complex behavior. Some have been proven to be universal computers, others exhibit properties familiar from traditional science, such as thermodynamic behavior, continuum behavior, conserved quantities, percolation, sensitive dependence on initial conditions, and others. They have been used as models of traffic, material fracture, crystal growth, biological growth, and various sociological, geological, and ecological phenomena.

Stephen Wolfram, in A New Kind of Science argues that, in order to capture the essence of almost any complex system it is necessary to systematically explore these systems and document what they do. He believes this study should become a new branch of science, like physics or chemistry. The basic goal of this field is to understand and characterize the computational universe using experimental methods.

The proposed new branch of scientific exploration admits many different forms of scientific production. For instance, qualitative classifications like those found in biology are often the results of initial forays into the computational jungle. On the other hand, explicit proofs that certain systems compute this or that function are also admissible. There are also some forms of production that are in some ways unique to this field of study. For instance, the discovery of computational mechanisms that emerge in different systems but in bizarrely different forms.

What's wrong

As of the first decade of the 21st century, the main challenge posed to Positivism (by its own ranks; "metaphisical" and teleological claims being, naturally, disqualified a priori) is the emergence of unsolved paradoxes from within seemingly "well-constructed" theories. Namely, Quantum Phisics and Bayesian Statistics result in disturbing, logic-defying results, that have generated a lot of havoc and schism within the positivist community.