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7

You're unsure about the definition of life (which the other answers clarify) but also most people are unclear about the definition of AI. Do you mean an AI that can accomplish a routine task (such as the path finder in a GPS) or a General AI that is able to find a creative solution to any directive given to it (such an AI does not yet exist and may not ever ...


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Not yet. Synthetic virology / Synthetic life are still in their infancy. We can now synthesize simple bacteria (see Craig Venter's fascinating TED talk and also an article about his recent work) but definitely nothing that may be called 'rational' in human standards.


6

Really short answer: yes Slightly longer answer: kinda Long answer: Convolutional neural networks (CNNs), which are now a standard in image processing models, were inspired from work done by Hubel and Wiesel in the 1950-60s. They showed that the visual cortexes of cats and mokeys contain neurons which individually respond to small regions of the visual ...


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Any logic circuit admits a variety of implementations. All programs executing on conventional digital processors can be expressed as logic circuits. Among the possible implementations of logic circuits are fluidic implementations, which do not depend on electronics per se. Thus it is in principle possible to implement, e.g. a POMDP processor (responsive ...


6

I recommend Animal Learning and Cognition: An Introduction by John Pearce. As an introduction to the field, it's comprehensive. It covers all the topics you mentioned and more. Here is a free preview of the book. It's well-regarded, having over 500 citations on Google Scholar. It's relatively modern. Published in 2008, it discusses experimental findings ...


5

The release of Adenosine, Dopamine, Endorphin, Endocannabinoids, GABA, Glutamate, Norepinephrine, Oxytocin, Serotonin, and many others into specific regions of the brain are very likely an essential part of both activation tuning of single neurons and neuroplasticity, two essential aspects of organic learning researchers have been and will continue to work ...


5

No, I think electricity is not essential for AI. In theory AI (a sufficient collection of computational processes that can adapt to changes in their input, thus producing 'intelligent' behavior), could be implemented using any mechanism that can compute that set of essential functions needed to create AI. Basically I'm suggesting the possibility of ...


5

Biological and artificial evolution work around pretty much the same principles. Fitness and selection: In biology, the fittest organisms in an ecosystem are more likely to survive long enough to reproduce, passing on their genes in the process. In artificial evolution, our organisms are in fact solutions to our problem, which can be evaluated to determine ...


4

"Life" is a definition humans use to classify objects according to the types of behavior humans perceive as unique to living creatures. Scientists and philosophers tend to define something as "alive" if it manifests some specific properties found in living organisms, such as self-replication, adaptation to the environment, homeostasis and ...


4

If you read Steven Levy's book, Artificial Life,you will find, as I did, the distinction between biological and "artificial" life blurred. If you think about it, what exactly is "life", anyway? A set of complex systems with emergent behavior capable of evolution and adaptation. A prototypical biologist may not define life that way. Indeed, he would, not ...


4

Only a small portion of the habituation, sensitization, and classical conditioning behavior of neurons has been primitively simulated in ANN systems. Simulation of actin cytoskeletal machinery1 and other agents of neural plasticity, central to learning new domains, is in its beginnings2. As of this writing, the complexity of neuron activation dwarfs the ...


4

Artificial intelligence by definition is the intelligence exhibited by machines. The definition of life in biological terms is the condition that distinguishes organisms from inorganic matter where the distinguishing criteria are the capacity for growth, reproduction, functional activity, and continual change preceding death. Does artificial intelligence "...


4

In short I mentioned in another post, how the Artificial Neural Network (ANN) weights are a relatively crude abstraction of connections between neurons in the brain. Similarly, the random weight initialization step in ANNs is a simple procedure that abstracts the complexity of central nervous system development and synaptogenesis. A bit more detail (with ...


3

What is life? AND Is AI a living organism? are two different questions. The first question is more philosophical and dependent. It can change with time, reference to topic of discussion or something else. Today, one parameter to its definition is mortality. In future if we reach to a certain technological level where mortal beings were only part of history, ...


3

Principles of Computational Modelling in Neuroscience by David Sterratt, Bruce Graham, Andrew Gillies and David Willshaw discuss it in Chapter 7 (The synapse) and also in Chapter 8 (Simplified models of neurons). Especially in chapter 8, they discuss how to add excitatory or inhibitory synapses to integrate and fire neuron. There are various ways to add ...


3

I think you are slightly confusing 2 problems. 1 being classification of meta visual elements and the other being the visual system itself. Our visual system, when it comes to processing information, has had billions of years of iteration(training), so that at birth(and before), we are already tuned for the processing of visual stimuli, as well as have the ...


3

There are a lot of examples of animals that have been trained by humans (to perform some specific task). For example, dogs, tigers or chimpanzees. Nonetheless, none of them have exhibited a general intelligence comparable to that of humans. Why is that? It is believed that the intelligence of mammals is (at least partially) determined by the size of the ...


3

In the case of artificial neural networks, your question can be (partially) answered by looking at the definition of the operation that an artificial neuron performs. An artificial neuron is usually defined as a linear combination of its inputs, followed by the application of a non-linear activation function (e.g. the hyperbolic tangent or ReLU). More ...


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There are variety of aspects where AI can help for a public good. Future studies of computational methods can contribute to sustainable management ecosystem by its data acquisition, interpretation, integration and model fitting. Prof. Tom Dietterich is a leader in combining computer science and ecological sciences to build new discipline of Ecosystem ...


2

ANNs approximate biological neuronal networks. The approximation began with extreme simplicity in the early perceptron design. Spiking networks are examples of more accurate approximations. More accurate still, are complex simulations of neuron behavior that therefore necessitate significant computing resources. If you are interested in a mathematical ...


2

One of the most common requirements to be defined as life is abbreviated to MRS GREN this means: M - movement R - respiration S - sensitivity G - growth R - reproduce E - excretion N - nutrition An AI can technically do some of these, it can move its location from device to device, it can grow its own code, and assimilate other bits of code it can find, ...


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A common predilection of what many presume extraterrestrial life is fits general descriptions specific to terrestrial life. No guarantee exists providing for potential extraterrestrial life having any notion of any attribute we commonly relate to living organisms we are currently aware of; including a composition of cells. The same misunderstanding applies ...


2

I like to take an "animist" approach. (It has been suggested to me that part of the reason Japanese designs are so effective is because of the cultural affinity for the concept per the Shinto tradition. For instance, the thing where people put little eyes on everything;) I like to think of how my dog, who is terrified of the vacuum cleaner, would regard ...


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Today, prominent machine learning techniques involve trying to minimize some cost function. In many simple cases this cost function is easy to specify, for instance, linear regression is simply trying to minimize the distance between input data and a line of best fit. No matter what the cost function, the agent is trying to minimize it (or maximize a reward ...


2

Wikipedia describes life as a characteristic of "physical entities having biological processes". The same source also describes a simulation as "the imitation of the operation of a real-world process or system over time." If a digital neural net was to listen to me prattle on for long enough it could learn to speak as if it were me. It would have my ...


2

In biology, when the presynaptic releases a neurotransmitter (a positive amount of them, obviously), this neurotransmitter reaches the postsynaptic vesicles causing an excitatory (depolarization) or inhibitory (hyperpolarization) effect, depending on the kind of postsynaptic vesicle in next cell dendrites. If the total amount of depolarization (all dendrites)...


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Biological life is the only form of intelligence life that we are aware of. Biology is often an inspiration to AI researchers to develop artificial intelligence. There are numerous examples of AI models and algorithms that have been introduced (at least, partially) based on or inspired by the biology. For example, reinforcement learning is based on the ...


2

A benchmark comparison of systems comprised of separately trained networks relative to single deeper networks would not likely reveal a universally applicable best choice.1 We can see in the literature the increase in the number of larger systems where several artificial networks are combined, along with other types of components. It is to be expected. ...


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There is indeed an investigation in progress, regarding this topic. A first publication from last march noted that modularity has been done, although not explicitly, since some time ago, but somehow training keeps being monolithic. This paper assess some primary questions about the matter and compares training times and performances on modular and heavily ...


2

Yes, for many sensory inputs there is indeed something similar to normalization. But its not rally the same as in classical data analytics compared to what eg min/max normalization does or other technics. Lets look on some examples and considerations: mammals don't perceive heat or loudness in a linear way. This is because already many sensory receptors ...


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