Special report : Can we copy the brain? - What intelligent machines need to learn from the Neocortex

IEEE Spectrum ◽  
2017 ◽  
Vol 54 (6) ◽  
pp. 34-71 ◽  
Author(s):  
Jeff Hawkins
Keyword(s):  
Special Report ◽  
Intelligent Machines ◽  
The Brain

scholarly journals Turing++ Questions: A Test for the Science of (Human) Intelligence

AI Magazine ◽  
2016 ◽  
Vol 37 (1) ◽  
pp. 73-77 ◽  
Author(s):  
Tomaso Poggio ◽  
Ethan Meyers
Keyword(s):  
Social Intelligence ◽  
Visual Recognition ◽  
Turing Test ◽  
Scientific Field ◽  
Human Intelligence ◽  
Intelligent Machines ◽  
Recognition Of Objects ◽  
The Mind ◽  
The Brain ◽  
Widespread Interest

It is becoming increasingly clear that there is an infinite number of definitions of intelligence. Machines that are intelligent in different narrow ways have been built since the 50s. We are entering now a golden age for the engineering of intelligence and the development of many different kinds of intelligent machines. At the same time there is a widespread interest among scientists in understanding a specific and well defined form of intelligence, that is human intelligence. For this reason we propose a stronger version of the original Turing test. In particular, we describe here an open-ended set of Turing++ Questions that we are developing at the Center for Brains, Minds and Machines at MIT — that is questions about an image. Questions may range from what is there to who is there, what is this person doing, what is this girl thinking about this boy and so on.  The plural in questions is to emphasize that there are many different intelligent abilities in humans that have to be characterized, and possibly replicated in a machine, from basic visual recognition of objects, to the identification of faces, to gauge emotions, to social intelligence, to language and much more. The term Turing++ is to emphasize that our goal is understanding human intelligence at all Marr’s levels — from the level of the computations to the level of the underlying circuits. Answers to the Turing++ Questions should thus be given in terms of models that match human behavior and human physiology — the mind and the brain. These requirements are thus well beyond the original Turing test. A whole scientific field that we call the science of (human) intelligence is required to make progress in answering our Turing++ Questions. It is connected to neuroscience and to the engineering of intelligence but also separate from both of them.

Special report : Can we copy the brain? - The brain as computer

IEEE Spectrum ◽  
2017 ◽  
Vol 54 (6) ◽  
pp. 28-33 ◽  
Author(s):  
Karlheinz Meier
Keyword(s):  
Special Report ◽  
The Brain

The Quest for Intelligent Machines

2017 ◽  
Author(s):  
Arlindo Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Trees ◽  
Bayesian Learning ◽  
Turing Test ◽  
Intelligent Machines ◽  
The Brain ◽  
Existing Data

This chapter addresses the question of whether a computer can become intelligent and how to test for that possibility. It introduces the idea of the Turing test, a test developed to determine, in an unbiased way, whether a program running in a computer is, or is not, intelligent. The development of artificial intelligence led, in time, to many applications of computers that are not possible using “non-intelligent” programs. One important area in artificial intelligence is machine learning, the technology that makes possible that computers learn, from existing data, in ways similar to the ways humans learn. A number of approach to perform machine learning is addressed in this chapter, including neural networks, decision trees and Bayesian learning. The chapter concludes by arguing that the brain is, in reality, a very sophisticated statistical machine aimed at improving the chances of survival of its owner.

scholarly journals How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence

2021 ◽  
Author(s):  
Abigail R. Gutai ◽  
Thomas E. Gorochowski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Intelligence ◽  
Evolutionary Theories ◽  
Intelligent Machines ◽  
Innovative Solutions ◽  
Biological Concepts ◽  
Multi Level ◽  
Evolution By Natural Selection ◽  
The Brain

Since its advent in the mid-twentieth century, the field of artificial intelligence (AI) has been heavily influenced by biology. From the structure of the brain to evolution by natural selection, core biological concepts underpin many of the fundamental breakthroughs in modern AI. Here, focusing specifically on artificial neural networks (ANNs) that have become commonplace in machine learning, we show the numerous connections between theories based on coevolution, multi-level selection, modularity and competition and related developments in ANNs. Our aim is to illuminate the valuable but often overlooked inspiration biologists have provided AI research and to spark future contributions at this intersection of biology and computer science. Although recent advances in AI have been swift, many significant challenges remain requiring innovative solutions. Thankfully, biology in all its forms still has a lot to teach us, especially when trying to create truly intelligent machines.

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