The Digital Mind: How Science is Redefining Humanity

Following the release in the US,  The Digital Mind, published by MIT Press,  is now available in Europe, at an Amazon store near you (and possibly in other bookstores). The book covers the evolution of technology, leading towards the expected emergence of digital minds.

Here is a short rundown of the book, kindly provided by yours truly, the author.

New technologies have been introduced in human lives at an ever increasing rate, since the first significant advances took place with the cognitive revolution, some 70.000 years ago. Although electronic computers are recent and have been around for only a few decades, they represent just the latest way to process information and create order out of chaos. Before computers, the job of processing information was done by living organisms, which are nothing more than complex information processing devices, created by billions of years of evolution.

Computers execute algorithms, sequences of small steps that, in the end, perform some desired computation, be it simple or complex. Algorithms are everywhere, and they became an integral part of our lives. Evolution is, in itself, a complex and long- running algorithm that created all species on Earth. The most advanced of these species, Homo sapiens, was endowed with a brain that is the most complex information processing device ever devised. Brains enable humans to process information in a way unparalleled by any other species, living or extinct, or by any machine. They provide humans with intelligence, consciousness and, some believe, even with a soul, a characteristic that makes humans different from all other animals and from any machine in existence.

But brains also enabled humans to develop science and technology to a point where it is possible to design computers with a power comparable to that of the human brain. Artificial intelligence will one day make it possible to create intelligent machines and computational biology will one day enable us to model, simulate and understand biological systems and even complete brains with unprecedented levels of detail. From these efforts, new minds will eventually emerge, minds that will emanate from the execution of programs running in powerful computers. These digital minds may one day rival our own, become our partners and replace humans in many tasks. They may usher in a technological singularity, a revolution in human society unlike any other that happened before. They may make humans obsolete and even a threatened species or they make us super-humans or demi-gods.

How will we create these digital minds? How will they change our daily lives? Will we recognize them as equals or will they forever be our slaves? Will we ever be able to simulate truly human-like minds in computers? Will humans transcend the frontiers of biology and become immortal? Will humans remain, forever, the only known intelligence in the universe?

 

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Arrival of the Fittest: why are biological systems so robust?

In his 2014 book, Arrival of the Fittest, Andreas Wagner addresses important open questions in evolution: how are useful innovations created in biological systems, enabling natural selection to perform its magic of creating ever more complex organisms? Why is it that changes in these complex systems do not lead only to non-working systems? What is the origin of variation upon which natural selection acts?

Wagner’s main point is that “Natural selection can preserve innovations, but it cannot create them. Nature’s many innovations—some uncannily perfect—call for natural principles that accelerate life’s ability to innovate, its innovability.”51bwxg5grcl-_sx324_bo1204203200_

In fact, natural selection can apply selective pressure, selecting organisms that have useful phenotypic variations, caused by the underlying genetic variations. However, for this to happen, genetic mutations and variations have to occur and, with high enough frequency, they have to lead to viable and more fit organisms.

In most man-made systems, almost all changes in the original design lead to systems that do not work, or that perform much worse than the original. Performing almost any random change in a plane, in a computer or in a program leads to a system that either performs worst than the original, or else, that fails catastrophically. Biological systems seem much more resilient, though. In this book, Wagner explores several types of (conceptual) biological networks: metabolic networks, protein interaction networks and gene regulatory networks.

Each node in these networks corresponds to one specific biological function: in the first case, a metabolic network, where chemical entities interact; in the second case, a protein interaction network, where proteins interact to create complex functions; and in the third case, a gene regulatory network, where genes regulate the expression of other genes. Two nodes in such networks are neighbors if they differ in only one DNA position, in the genotype that encodes the network.

He concludes that these networks are robust to mutations and, therefore, to innovations. In particular, he shows that you can traverse these networks, from node to neighboring node, while keeping the biological function unchanged, only slightly degraded, or even improved. Unlike man-made systems, biological systems are robust to change, and nature can experiment tweaking them, in the process creating innovation and increasingly complex systems. This how the amazingly complex richness of life has been created in a mere four billion years.

 

Human rights, animal rights, and robot rights.

An interesting article, in the New York Times, discusses the question of the rights of animals and robots.

The article describes the demise of Harambe, a gorilla from the Cincinnati Zoo, killed when a young boy fell in the moat that was part of the habitat that Harambe shared with two other gorillas. When they felt the boy was in danger, the zookeepers shot the gorilla, in an effort to save the life of the boy.

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Angry protesters complained that shooting the gorilla was a violation of his rights. Discussing the rights of gorillas is particularly eerie because gorillas are so close to us. They are among our  nearest cousins, having separated from humans a mere  7 million years ago.

Today, these musings have another worrying side. We view ourselves as owners and sometimes protectors of the lower animals. Soon, however, robots may view ourselves in the same way we view gorillas, or other animals. Should robots be allowed to kill humans, if and when they endanger other humans or other animals? What are the moral questions at stake here? When is it legitimate to kill a human or, for that matter, an animal? And what about the rights of robots? It is ok to terminate them, as long as they are not sentient?

The key question is: how inhuman is it to kill an animal or to terminate a robot? Does it depend on the level of sentience the animal or the robot has? Most people believe lower animals are not conscious, but the jury is still out on that question. Are bats conscious? Do they experience some sort of consciousness when they fly in the dark, sensing the environment with ultrasounds? Can we ever get to know that is feels like to be a bat?

Thomas Nagel article ““What Is It Like to Be a Bat?”, discusses exactly this subjective aspect of consciousness, concluding that we can never fully understand what consciousness is, from the subjective point of view of the entity living the experience. Other philosophers, such as Daniel Dennett disagree, and believe that consciousness can, and will be, understood, since it is nothing more than an emerging phenomenon, a result of the operation of complex systems.

Illustration by Nishant Choksi, for the New York Times.

 

Darwin and the Elephants

The basic idea underlying Charles Darwin theory of evolution is that the number of individuals in a given species would grow exponentially, in the absence of pressures against population growth. Only selective pressures may curb this exponential growth, and select some species over the others.

In Charles Darwin’s own words, in the Origin of the Species:

“There is no exception to the rule that every organic being increases at so high a rate, that if not destroyed, the earth would soon be covered by the progeny of a single pair. Even slow-breeding man has doubled in twenty-five years, and at this rate, in a few thousand years, there would literally not be standing room for his progeny. Linnaeus has calculated that if an annual plant produced only two seeds – and there is no plant so unproductive as this – and their seedlings next year produced two, and so on, then in twenty years there would be a million plants. The elephant is reckoned to be the slowest breeder of all known animals, and I have taken some pains to estimate its probable minimum rate of natural increase: it will be under the mark to assume that it breeds when thirty years old, and goes on breeding till ninety years old, bringing forth three pairs of young in this interval; if this be so, at the end of the fifth century there would be alive fifteen million elephants, descended from the first pair.”

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Even though Charles Darwin got his numbers wrong, as pointed out by William Thomson, later to become Lord Kelvin, the idea is entirely correct. The number of elephants at generation n is given by the formula a(n) = 2 × a(n-1) – a(n-3). 

This succession of numbers converges rapidly to a ratio of 1.618 between the number of elephants at generation n and the number at generation n-1.

If one plugs the numbers in, one realizes that even though only 14 elephants are alive after one hundred years and 8360 after five hundred years (not 15 million, as Darwin stated), there would be almost 30 million elephants alive after a thousand years. After three thousand years, there would be a billion trillion elephants, which would have a combined mass equal to that of planet Earth. Assuming they can grow indefinitely, after only seven thousand years, the solid sphere of roughly 10 to the 50th elephants, by that time with a diameter of 200 light-years, would expand outward faster than the speed of light.