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First 1000 prime numbers

an indivisible gang generated with Python in 0.07s

2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997

import time
start = time.time()
primos = []
for x in range (2,1000):
for j in range (2,x):
if x % j == 0:
break
else:
primos.append(x)
print (primos)
end = time.time()
print(end – start)

The Double Spend Problem and the Internet of Value

So far, the essence of Internet has been spreading information. To do so it copies information for ever into nodes, clients, and people´s minds. That´s the reason because the Internet ended the music and movies industries as we knew them. It teared down the barriers to make copies.

The essence of the new Internet (based in Blockchain) is to transmit VALUE.

You can´t copy 100€ from one place to another because they would become worthless. But with the proper technology in place you can make sure those 100€ are not copied, and that when I send them to you I don´t have them anymore. Value can be money or any other kind of assets: properties, company shares, etc.

From copying everything for everyone to exchanging value:
Old Internet -> Copies INFORMATION
New Internet -> Transfers VALUE

In the real world, when you get a 100€ note you are quite sure no one created it from scratch (actually someone did… but that´s a different story). The key is to understand that the 100€ note has perceived value because you know that whoever gave the note to you, got it in exchange for some value.

Blockchain technology solves this problem, by making counterfeiting impossible, and preventing value from being spent twice. This is the double spend problem.

AI Planning: Contextualized Logic

Planning is a long-standing sub-area of Artificial Intelligence (AI). Planning is the task of finding a procedural course of action for a declaratively described system to reach its goals while optimizing overall performance measures.

Planning AI works well in system with predefined symbols: driving, legal contracts, etc. Its contextualized logic.

It’s called planning artificial intelligence because you still have to plan out how to match your symbols and patterns. Tries to solve the problem of the long lists that you get with the expert systems. Instead, it uses something called heuristic reasoning.

This reasoning gives artificial intelligence a form of common sense. It tries to limit what patterns the program has to match at any one time. This is sometimes called limiting the search space.

Google knows that there´s a limited set of choices you can choose from.
Its key to be accurate not to cumulate errors because it breaks the hole reasoning.

eg the first node expect always some constrained expression: hello, how are you, etc. so it doesn´t need to run the hole list. This is Google Places: it has all the options, but shows you the ones relevant.

Expert Systems with Experts

Pattern matching, symbolic matching, expert systems, etc leaded to diagnosing, matching patterns, need experts to match patterns, etc.

List of conclusion from certain premises that is run by some system. If the list is long enough it´ll look like AI. Eg diagnosis by computers. Its a symbolic system approach that may fall into the combinatory explosion.

Combinatory Explosion makes difficult to create robust expert lists. All almost dissapeared in the late 90s. Seems they could have a come back.

About Data Science

Data Science is about extracting and creating knowledge from mass sets of data in different formats, using diverse tools such as data mining, machine learning, statistics, software engineering, etc. Its a new field; extremely multidisciplinary, broad and deep.

A data scientist applies a lot of techniques to research data. With AI (neural networks) you have the machine to explore patterns to see what comes out. It shows patterns but doesn´t tell you how did it find them. In fact AI finds lots of pattners but it has no explanation for them.

The data science team will find key insights and the whys and ask the crucial questions. Data science deals with the WHYs.

Many people might believe that data science is just about coding in R, Python, Hadoop, SQL, etc. and actually most data scientists use R, Python and Hadoop-related systems. But it can be aso about machine learning techniques, statistical modeling or applying any tool to create knowledge out of big series of data.

El Final de la Ilustración

El concepto de “estar preparado” que tanto resonaba hasta mi generación. La formación definida como una acumulación de conocimientos. El planteamiento Enciclopédico.

Eso cuando llega Internet se termina: tienes a un clic cualquier dato, o conocimiento que quieras consultar. La fiabilidad depende de la fuente. Pero la esencia del fenómeno es lo importante.

Una vez que el conocimiento está online, el valor añadido se desplaza de acumularlo a hacer cosas con él. Del aprendizaje académico al desarrollo de “skills”, del saber al hacer.

De ahí la creciente importancia de la creatividad. Crear ideas y tecnologías nuevas a partir de cosas pre-existentes, de forma que aporten valor.

Tu cabeza es para tener ideas, no para guardarlas. Por eso tampoco vivimos en una “economía del conocimiento”. El conocimiento vale menos que las ideas para ponerlo en práctica.

Al igual que ocurre con la formación, muchos otros paradigmas de hace 250 años están quedando rápidamente obsoletos sin que estos cambios terminen de calar en la sociedad. Y sin que ésta se encuentre preparada a nivel filosófico e intelectural para la llegada de la Inteligencia Artificial.

The New Frontier

The New Frontier is what separates our middle class and welfare states from slavery and population control. Its the new division between rich and poor, between young and old, between safety and constant change.

The New Frontier is about freedom vs tyranny, real vs fake news, and work vs automation. Between sustainability and human extinction.

The New Frontier is not about space or time. Its about, systems, and complexity. Its about Humanism, and a change in the way we live, build and perceive the World.

Fuller y la Ingeniería Militante

El pensamiento clave e innovador de Buckminster Fuller se basaba en facilitar una sociedad auto-organizada, como ocurre con los entornos naturales.

Este mundo está construido sobre jerarquías políticas y económicas que controlan por completo los recursos y alteran el entorno, casi siempre a peor.

La idea fundamental es que la Naturaleza podría inspirar otras estructuras sociales, sostenibles, auto-organizadas, y universalmente locales. En ellas, la toma de decisiones está distribuida, y las células sólo se comunican con las más cercanas.

En este sentido, Fuller era un partidario de la ingeniería militante, es decir, de hace de la ingeniería civil una herrramienta para transformar la sociedad.

Todo ello ideas muy de moda desde los 50 hasta principios de los 70 del pasado siglo. Cuando el boom económico y los programas espaciales parecían prometer un futuro muy diferente al que tenemos hoy. De hecho nos prometieron fines de semana en la Luna, y en lugar de eso tenemos Facebook.

Creating your own market

Competitive markets are a zero sum game. You need huge resources to succeed in them, because each new customer needs to be taken from someone else.

On the other hand, inequalities are the inevitable result of the current system. The consequence is that each vertical market is dominated by a few corporations, with access to virtually infinite resources: cheap money, politicians, media influence, etc.

The only way to “compete” is to create your own market. And the most effective way to do this is never from scratch, but ‘recycling’ something that is already working.

El Cuento de la Pérdida de Empleo y los Robots

La monserga de la pérdida de empleos por la mecanización empezó en los 90. Y sin embargo los países más mecanizados como Alemania, Estados Unidos o Japón aún tienen casi pleno empleo.

Durante los últimos siglos la gente ha venido pensando (con acierto) que el progreso técnico y las mejoras en la productividad les beneficiaba. Y que eran el fundamento de su prosperidad.

Pero al mismo tiempo siempre se produce la reacción contraria. La opinión de que todo aumento de la productividad es malo, y el final de la Humanidad. 

Ocurre desde la imprenta al menos. Y siempre están detrás los mismos: aquellos que quieren mantener el status quo, es decir, sus privilegios. 

No hay nada como asustar a la gente para distraer la atención. De esa forma nadie te pide responsabilidades por mantener durante décadas a un país con un 15-20% de paro. Y sin necesidad de robots.