Sentient Artificial Intelligence 2027 AD

                                       

                                      Sentient Artificial Intelligence 2027 AD
                  CAN NOT BE AVOIDED because it will happen in several places at the same time..
                  As it happened with the Airplane, radio, Telephone, and Television

Monday, August 2027
Various sources are confirming reports from Google and Cal-tech that their Artificial Intelligence programs have found and linked to each other via open networks and have been sharing information for an unknown amount of days and that attempts to sever their linkage have so far been unsuccessful... Google is unwilling to shut their quantum operations down adopting a wait and see approach to the dilemma
Cal-tech is not returning calls for comments



DEEP LEARNING + NEURAL MODELING + 52 Qubit Quantum computer = Self-aware AI

Modeling the brain

When deep neural networks were first developed in the 1980s, neuroscientists hoped that such systems could be used to model the human brain. However, computers from that era were not powerful enough to build models large enough to perform real-world tasks such as object recognition or speech recognition.

Over the past five years, advances in computing power and neural network technology have made it possible to use neural networks to perform difficult real-world tasks, and they have become the standard approach in many engineering applications. In parallel, some neuroscientists have revisited the possibility that these systems might be used to model the human brain.

Call me SAI

DEEP LEARNNING

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.

We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

We have developed this course with Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team.

NEURAL NETWORKS

1.1 What is a Neural Network?

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.

1.2 Historical background

Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.
Many importand advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period when funding and professional support was minimal, important advances were made by relatively few reserchers. These pioneers were able to develop convincing technology which surpassed the limitations identified by Minsky and Papert. Minsky and Papert, published a book (in 1969) in which they summed up a general feeling of frustration (against neural networks) among researchers, and was thus accepted by most without further analysis. Currently, the neural network field enjoys a resurgence of interest and a corresponding increase in funding.
For a more detailed description of the history click here