Endor can upgrade the scientific base of Google and Facebook, from astrology to astronomy, and from alchemy to chemistry
Social Physics is better than deep machine learning, because the emergent network effect
The biology of the natural neural networks was critical for building artificial neural networks, and thus, for the breakthrough of the last decade in AI. Now the biology of natural crowds and swarms is critical for building artificial crowds and swarms (digital social networks), as the basis of much bigger breakthroughs in AI.
It is much bigger because social networks are network of Neural Networks, or meta neural network. In all the kinds of network we have the laws of network effect and emergence. Thus, the network effect will be much bigger in social network than in neural networks, since in emergent phenomena, the whole is much bigger than its parts. I call these artificial crowds and swarms Deep Social Networks, that enable “very deep machine learning”. The science behind artificial crowds is social physics.
“In recent years, data scientists have started to employ “heavy-weight” statistical methods and Machine Learning algorithms to try and cope with this complexity. These powerful tools, including the new “Deep Learning” techniques, collect data and analyze its attributes to be able to classify behavioral patterns, detect anomalies, and predict future trends. However, such tools — historically developed for “static problems” such as image processing and text recognition — cannot easily cope with human behavior data: learning dynamic, complex, and versatile data streams is extremely hard and sometimes nearly impossible.
Endor’s Social Physics engine works in a completely different way. Instead of deriving patterns from input data itself, it is based on the discovery that all human behavioral data is guaranteed to contain within it a set of common “social behavioral laws” -mathematical relationships that emerge whenever a large enough number of people operate in the same space. These laws govern the way various statistical properties of crowd behavior evolve over time, regardless of the type of data, the demographics of the users who created it, or the data size. Endor has integrated these laws into its data analytics engine, which efficiently extracts the underlying social attributes of all people contained in the raw data being provided as input (e.g. phone calls, taxi rides, financial investments, etc.).” ” Pages 48–49, https://uploads-ssl.webflow.com/5b1cfaa93299683f6c2a5653/5b1cfaa9329968a6f72a57d0_5a8a97fc54ea7a000146ac08_Endor_Protocol_WP.pdf
The Blockchain is the deep social network of money and of other kinds of values.
Thus, the blockchain is an emergent phenomenon from the neural networks in the brains of the users of crypto, the miners and the nodes. The social physics can cope with three problems of such complex entity like Blockchain and other kinds of DLT (Distributed Ledger Technologies).
A, it can cope complexity by reducing it according to few basic laws of social physics that are like the three Newtonian basic laws of physics.
B, it can cope with the dynamic nature of social entities (like stocks exchanges, Bitcoin, election, etc.’), by the constants of the social physics, that are like the Gravitational constants of physic, G.
C, it can cope at the same time with both the generic nature of social entities and with the huge-data that they create, by finding the optimal ratio between a general theory of Collective Intelligence and the amount of data that is needed for certain problem.
A Double Black Swan
History shows that FAMGA (Facebook, Amazon, Microsoft, Google, Apple) will not see the Black Swan of the BlockChain, because Black swans cannot be seen in the darkness. IBM the biggest player in hardware, didn’t see the software revolution of Microsoft in 1985. Microsoft the giant of software, didn’t see the network revolution of Google in 1999. Google didn’t see the social network revolution of Facebook in 2005. Now, in 2018, Facebook didn’t see the Blockchain revolution of XXXX, The crypto revolution just began. https://medium.com/crypto-oracle/the-coming-epic-battle-between-crypto-famga-aka-facebook-apple-microsoft-google-amazon-1a05489c3abb
The Blockchain is the fifth layer of the Digital Ecosystem that emerge since 1945
In the digital world, like in the biological world, the development of an organism (ontogenesis) recapitulate the whole evolution (phylogenesis). The same is happening since 1945 in the evolution of the digital computers and networks.
Blockchain is the fifth layer in this evolution:
1. The hardware layer 1945–1978: The first stage of the digital evolution. The mainframe was devoured by yellow perforated cards and the output of huge, half-meter-long paper.
2. The software layer 1979–1993: Then in 1978 appeared the Apple computer that was followed by PC-DOS, as quick and smart computers that killed the mainframe. Microsoft has realized that operating system software is more important than hardware.
3. In 1990–1993, appeared the third layer, the Internet layer, by the inventions of the Web, the Browser, and the search engine.
4. The social layer appeared through the networked operating system called “social network” that catapulted Apple (iTunes, AppStore), Amazon (cloud services, Echo), Google (search) and Microsoft (LinkedIn, Office on the Net) to a value of about $ 1 trillion.
5. The fifth layer is the trust layer of the Blockchain, that blocks the giants of the past, and will creates the new Blockchain giants of $ 2 trillion.
So, we are now in the middle of an important synergy of two revolutions: the revolution of trust network of the Blockchain that can be analyzed by the social physics of the “deep social networks”.
The Perceptron was the simple one-layer artificial neural networks that was invented in 1956. The Perceptron enabled just a primitive and a weak kind of AI, and was replaced in 1986 by the multi layered deep neural network of Hinton et al.
The Sociotron is for deep social network, what the Perceptron was for deep neural networks. The “philosophy of social physics” that I created in 2010, and the “science of social physics” that Pentland et al. created in 2013, are just in the stage of the primitive Sociotron. Now Endor is in the right way towards the “technology and business of social physics” of the Billions of Dollars that the deep social networks will create.
Open suggestions for future development
1, If Endor want to be “the Google of Predictive Analytics” it should present a “prediction engine” (like search engine) on the web, that should take 15 second, not 15 minutes.
2, If Endor is better in analytics of people’s behavior, how it will analyses DLT like DAG?