Showing posts with the label biology

How Much Do Wild Animals Suffer? A Foundational Result on the Question is Wrong.

NOTE: I would like to clarify that the post below and the published paper show that a result from 1995 does not hold, but they do NOT make the case for the 1995 model being correct. There are many reasons the models in both papers are likely to be deeply flawed: path dependency, dynamic ecosystems, philosophical problems with the definition of suffering and enjoyment, and so on. The primary point here is to treat the 1995 result and other work on wild animal suffering with caution. In 1995, Yew-Kwang Ng wrote a groundbreaking paper, "Towards welfare biology: Evolutionary economics of animal consciousness and suffering"  that explored the novel question of the wellbeing of wild animals as distinct from the conservation of species. As perceptive as it was innovative, the paper proposed a number of axioms about evolution and consciousness to study which animals are sentient, what their experiences are, and what might be done about it. Among the many results in the paper wa

Is a Computer Neuron the Same as a Brain Neuron?

When I took a philosophy of mind class in high school, my professor proposed neural networks in computer science as a potential way to create consciousness. At the very least, it's a way to create high levels of intelligence. I didn't know exactly what a computerized neural network consisted of (I imagined it being built in hardware), and I still don't, really, but I'm curious: how similar is an artificial neural network to a biological one? Is it really a good replication? From an article on the similarities and differences :  An [Artificial Neural Network] consists of layers made up of interconnected neurons that receive a set of inputs and a set of weights. It then does some mathematical manipulation and outputs the results as a set of “activations” that are similar to synapses in biological neurons. While ANNs typically consist of hundreds to maybe thousands of neurons, the biological neural network of the human brain consists of billions. On the other han