Saturday, 28 December 2013

112. Evolution of Language - 1

'If there had been no speech, then right and wrong, truth and falsehood, good and bad, attractive and unattractive would not have been made known. Speech makes known all this. Worship speech' (Chandogya Upanishad, VII-2-1).

'A mostly Lamarckian process whereby evolution of a transformational nature proceeds via the passage of acquired characters, cultural evolution, like the stellar evolution before it, involves no DNA chemistry and perhaps less selectivity than biological evolution. Culture enables animals to transmit survival kits to their offspring by nongenetic routes; the information gets passed on behaviourally, from brain to brain, from generation to generation, the upshot being that cultural evolution acts much faster than biological evolution' (Eric Chaisson (2002), Cosmic Evolution).

Story-telling or spoken language was the first major invention of humans that enabled them to represent ideas with distinct utterances. And when written language was invented, we developed distinct shapes to symbolize our ideas. The evolution of language, speech, and culture are some of the causative factors in the rapid evolution of the size and capacity of the human brain. The emergence of human language has been a major milestone in the relentless evolution of complexity on our planet.

We 'know' what our thoughts and memories mean. But if we want to share them with others, they have to be translated into language. Our neocortex accomplishes this using what Kurzweil (2012) calls 'pattern recognizers', which have been trained with patterns that we have learnt for the purpose of using language.

According to Kurzweil (2012), language is highly hierarchical and it evolved to take advantage of the hierarchical nature of the neocortex, which in turn reflects the hierarchical nature of reality. Noam Chomsky wrote about the innate ability of humans to learn the hierarchical structures in language. This ability reflects the structure of the neocortex. Hauser, Chomsky and Fitch (2002) cited the attribute of 'recursion' as accounting for the unique language faculty of the human species. Recursion, according to Chomsky, is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure, and so on, iteratively and hierarchically. That is how we are able to build the elaborate structures of sentences and paragraphs and sections and chapters from a limited set of words.

According to Richard Dawkins (1989), ‘most of what is unusual about man can be summed up in one word: “culture”.’ Of course, one must make a distinction between ‘culture’ and ‘society’. ‘A society refers to an actual group of people and how they order their social relations. A culture . . . refers to a body of socially transmitted information’ (Barkhow 1989). The term ‘culture’ encompasses ‘all ideas, concepts and skills that are available to us in society. It includes science and mathematics, carpentry and engineering designs, literature and viticulture, systems of musical notation, advertisements and philosophical theories – in short, the collective product of human activities and thought’ (Distin 2005).

It is notable that, on the biological evolutionary time scale, there has been an exceptionally rapid expansion of brain capacity in the course of evolution of one of the ape forms (chimpanzees) to Homo sapiens, i.e. ourselves. This has happened in spite of the fact that the genome of humans is incredibly close to that of chimpanzees. The evolution of language, speech, and culture are believed to be some of the causative factors for this rapid evolution of the human brain. Let us see how.

Homo sapiens was preceded by Homo heidelbergensis, which also had a fairly large brain, but was not very effective as a hunter. He was not able to establish ecological dominance over other animals, even after two million years of evolution. Our human advantage is believed to have arisen from the emergence of language. ‘No topic is more intriguing and more difficult to address concretely than the evolution of language, but … [it] is almost a kind of sixth sense, since it allows people to supplement their five primary senses with information drawn from the primary senses of others. Seen in this light, language becomes a kind of “knowledge sense” that promotes the construction of extraordinarily complex mental models, and language alone may have provided sufficient benefit to override the cost of brain expansion’ (Klein and Edgar 2002).

The reference to ‘the cost of brain expansion’ here is to the fact that in humans the brain takes up ~20% of the metabolic resources of the body, and the brain tissue requires 22 times more energy than a comparable piece of muscle at rest.

Deacon (1997) emphasizes the big difference between human language (talking) on one hand and the various modes of communication among other live entities: ‘Although other animals communicate with one another, at least within the same species, this communication resembles language only in a very superficial way - for example, using sounds - but none that I know of has the equivalents of such things as words, much less nouns, verbs, and sentences. Not even simple ones.’

Deacon (1997) continues: ‘Though we share the same earth with millions of living creatures, we also live in a world that no other species has access to. We inhabit a world full of abstractions, impossibilities, and paradoxes … We tell stories about our real experiences and invent stories about imagined ones, and we even make use of these stories to organize our lives. In a real sense, we live our lives in this shared virtual world. … The doorway into this virtual world was opened to us alone by the evolution of language, because language is not merely a mode of communication, it is also the outward expression of an unusual mode of thought  -  symbolic representation. Without symbolization the entire virtual world is . . .  out of reach: inconceivable . . . symbolic thought does not come innately built in, but develops by internalising the symbolic process that underlies language’.

Homo heidelbergensis had a big brain. But was he also a great symbolic thinker? Probably not. Deacon argues that probably a single symbolic innovation triggered a coevolution of language and brain-size. Greater brain power resulted in a greater capacity to symbolise, speak, think. The cascading effect led to more complex languages and more complex brains. But all this required social interaction and support: ‘Language is a social phenomenon. … [and] … The relationship between language and people is symbiotic’.

I shall continue with this narrative in the next post.

Friday, 20 December 2013

111. Reconfigurable Computers and Machines That Can Evolve

'The malleability of configurable processors means that they can adapt at the hardware level. Does the term adaptation ring a bell? Over the past few years, researchers working on bio-inspired systems have begun using configurable processors; after all, they reasoned, what could be better suited to implement adaptive systems than soft hardware? And what do you get when you marry configurable processors with evolutionary computation? Evolvable hardware' (M. Sipper (2002), Machine Nature).

In a typical device having a microprocessor, e.g. a washing machine or a digital camera, the control part comprises a hardware and a software. The hardware is usually fixed and unchangeable. The software has been written by somebody and is therefore fixed (although new software can be loaded), and the user can only key-in his/her preferences about some operational parameters. Computational science has been progressively moving towards a scenario in which the hardware is no longer absolutely hard; it is changeable, even evolvable.

Evolutionary ideas have permeated a whole host of scientific and engineering disciplines, and computational science is no exception. I discussed the evolutionary aspects of software in Part 76. On-the-job changeability and evolution of hardware configurations offers exciting possibilities.

Conventional general-purpose stored-program computers have a fixed hardware, and they are programmable through software. Their microprocessors can be led through just about any conceivable logical or mathematical operations by writing a suitable set of instructions. Since their general-purpose, low-cost, hardware configuration is not fine-tuned for any specific task, they tend to be relatively slow.

For major specialized jobs involving a very large amount of number-crunching, it is more efficient to design application-specific ICs (ASICs), but then the overall long-term cost goes up, particularly if there is a need for upgradation or alteration from time to time.

An intermediate and very versatile approach is to have reconfigurable computers (RCs). A computer has a memory and a processor. The software is loaded into the memory; and the processor normally has a fixed, unalterable, configuration. By ‘configuration’ we mean the way the various components of the processor are interconnected, and the logical and mathematical operations they perform. If we can make these interconnections and operations (gate arrays) alterable, without having to physically change them by hand, we get an alterable or reconfigurable hardware. This is equivalent to having customized hardware which can be reconfigured at will (within limits), without incurring additional costs.

In RCs one makes use of 'field-programmable gate arrays' (FPGAs), the logic structure of which can be altered and customized by the user. The generic architecture of such RCs has four major components connected through a programmable interconnect: multiple FPGAs; memories; input/output channels; and processors.

FPGAs are highly tuned hardware circuits that can be altered at almost any point during use. They comprise of arrays of reconfigurable logic blocks that perform the functions of logical gates. The logic functions performed within the blocks, as well as the connections among the blocks, can be changed by sending control signals. A single FPGA can perform a variety of tasks in rapid succession, reconfiguring itself as and when instructed to do so.

Villasenor and Mangione-Smith (1997) made a single-chip video transmission system that reconfigures itself four times per video frame: It first stores an incoming video signal in the memory; then it applies two different image-processing transformations; and then becomes a modem to send the signal onward. FPGAs are ideally suited for algorithms requiring rapid adaptation to inputs.

Such softening of the hardware raises visions of autonomous adaptability, and therefore evolution. It should be possible to make the soft hardware evolve to the most desirable (fittest) configuration. That would be a remarkable Darwinian evolution of computing machines, a marriage of adaptation and design.

Self-healing machines

Biological systems are robust because they are fault-tolerant and because they can heal themselves. Machines have been developed that can also heal themselves to a certain extent. The new field of embryonic electronics has drawn its sustenance from ontogeny (i.e. the development of a multicellular being from a single fertilized cell, namely the zygote) and embryogenesis observed in Nature. One works with a chessboard-like assembly of a large number of reconfigurable computer chips described above. This is like a multicellular organism in Nature. The chips or cells are blank-slate cells to start with. We specify a task for the assembly; say, to show the time of the day. We also wish to ensure that the artificial organism is robust enough to heal itself; i.e. it should repair itself if needed. Such a BioWatch has indeed been built.

Ontogeny in Nature involves cell division and cell differentiation, with the all-important feature that each cell carries the entire genome. The BioWatch borrows these ideas. The genome, of course, comprises of the entire sequence of instructions for building the watch. One starts by implanting the genome (the zygote) in just one cell. Cell division is simulated by making the zygote transfer its genome to the neighbouring cells successively. When a cell receives the genome from one of its neighbours, information about its relative location is also recorded. In other words, each of the cells knows its relative location in the assembly.
This information determines how that cell will specialize by extracting instructions from the relevant portion of the genome. Thus each cell or chip, though specialized (‘differentiated’) for doing only a part of the job, carries information for doing everything, just as a biological cell does. The BioWatch is now ready to function. Its distinctive feature is that it can repair itself. How?

Suppose one of the cells malfunctions, or stops functioning. There are kept some undifferentiated (and therefore unused) cells in the same assembly. Repair or healing action amounts to simply ignoring the dead cell after detecting its relative position, and transferring its job to one of the fresh cells which already has the complete genome, and has to only undergo differentiation for becoming operational.

One can do even better than that by making the system hierarchical. Each cell can be given a substructure: Each cell comprises of an identical set of ‘molecules’. When one of the molecules malfunctions, its job is transferred to a fresh molecule. Only when too many molecules are non-functional does the entire cell become dead, and the services of a fresh cell are requisitioned.

Hardware you can store in a bottle

Adamatzky and coworkers (2005) have been developing chemical-based processors that are run by ions rather than electrons. At the heart of this approach is the well-known Belousov-Zhabotinsky or BZ reaction. A BZ reaction is a repeating cycle of three sets of chemical reactions. After the ingredients have been brought together, they only need some perturbation (e.g. a catalyst, or a local fluctuation of concentration) to trigger the first of the three sets of reactions. The products of this reaction initiate the second set of reactions, which then set off the third reaction, which then restart the first reaction. And so on, cyclically.
The BZ reaction is self-propagating. Waves of ions form spontaneously and diffuse through the solution, inducing neighbouring regions to start the reactions.

Adamatzky has been developing liquid logic gates (for performing operations like ‘not’ and ‘or’) based on the BZ reaction. It is expected that an immensely powerful parallel processor (a liquid robot brain) can be constructed, which will be a blob of jelly, rather than an assembly of metal and wire. Such a system would be highly reconfigurable and self-healing.

A possible host material for this blobot is a jelly-like polymer called PAMPS, an electroactive gel. It expands or contracts when an electric field is applied. BZ waves can travel through it without getting slowed down substantially, and the waves can be further manipulated interactively by internal or external electric fields. One day we may end up having an intelligent, shape-changing, crawling blob based on such considerations.

Who would have thought that the FPGA-chip idea would one day find a direct analogue in the way the human brain has evolved? But that is exactly what has happened. As explained in a recent and highly successful theory of the human brain, a child starts out with a huge number of 'connections-in-waiting' to which the 'pattern recognition modules' can hook up. I look forward to telling you all about it in a future set of posts when I discuss learning, memory, intelligence etc.

Saturday, 14 December 2013

110. Cultural Evolution and Complexity Transitions

A vast number of our social institutions are shaped by the principles of self-organization. In recent times, the development of the Internet and the increased capacity for creating new information and spreading existing or new information has propelled us into a new era of social organization. For example, the dynamics of information exchange creates new social trends organically, without any top-down directionality. The lessons gained from studying the emergence of such complex institutional entities from the actions of individuals acting out of self-interest can be harnessed towards achieving efficiency in corporate management or when drafting public policy.

Investigation of one type of complex system can provide insights into what may be happening in other complex systems. An obvious case in point is: How to understand human intelligence as a kind of swarm intelligence. Human intelligence emerges from the interactions among neurons, in spite of the fact that any particular neuron is as dumb as can be.

Understanding any complex system is a 'hard' research problem, and therefore progress usually comes in small increments, and also by comparing the behaviour of one complex system with another, looking for common threads. And in the evolution of complex systems one can often identify the so-called ‘complexity transitions’ (Bar-Yam 1997), which usually have far-reaching consequences. Emergence of life from nonlife was one such transition. Another complexity transition, which is still not complete, is that from monarchy or dictatorship to democracy.

[In Part 30 I had explained that 'bifurcations in phase space' is a more general term than 'phase transitions'. 'Complexity transitions' means the same thing as 'bifurcations in phase space'.]

In the corporate sector, and also in other human organizations, there is occurring a transition away from hierarchical control. In a hierarchical complex system it is implied that the degree of complexity of the controlling individual is more than that of the organization. As the complexity of the subsystems, as also their interdependence and communication channels, increase, such a scenario becomes untenable. The result is ‘horizontally’ interacting subsystems, rather than top-down control systems.

Human civilization can indeed be regarded as a single complex system (Bar-Yam 1997). A hurdle to the investigation of such a system is that it is one of a kind; there is nothing similar to compare it with.

Our ever-increasing cultural complexity has also resulted in a highly networked global economy. This is a case of complexity transition from hierarchical control to networked transactions.

Shown below is a complexity transition in human organizations (after Bar-Yam 1997).

As depicted in this diagram, the ever-increasing complexity of human organizations has resulted in a (still ongoing) complexity transition:

(a) A single person (king / dictator / big boss) takes all the decisions and directs the behaviour of all persons under his domain. The actions of the controlled persons are simple at both the individual and the collective level.

(b) As the complexity of options and behaviour increases, intermediate layers of hierarchical control emerge. The intermediate layers filter the information reaching the top layer, and also elaborate on the nature of the commands down the line. This can work only if the collective behaviour can be simplified in an effective manner.

(c) There is a veritable complexity transition when the maximum degree of complexity of an individual becomes insufficient, i.e. is less than, the collective complexity. Then the filtering of the information way up, and the elaboration of the directives on the way down, become ineffective.

(d) Ultimately there is a network of individuals, in which everybody can communicate with everybody else directly. This results in qualitatively new emergent behaviour and characteristics. An analogy with the neural network of the human brain immediately points to the possibility of emergence of supra-human intelligence in the human network.

Consequences of this complexity transition in our civilization

Prior to the transition, the complexity of the various organized structures was less than the complexity of a typical human being. After the transition the opposite is the case. There is now practically a weakening of the central control. This has consequences for the individual, as well as for the more complex environment in which the individual must function (Bar-Yam 1997).

The individual was, till recently, the most complex single organism. But now the environment is more complex than what was the most complex so far. An analogy with the rest of the animal kingdom can help us understand the response of the human individual. All the other animals are less complex than the environment. They survive as species by reducing their interaction with the complex environment (e.g. by creating for themselves certain ecological niches), and also by reproducing excessively.

The humans have also been striving for more and more specialization, so that they can sell their skills competently and survive. This also helps them limit their exposure to the highly complex modern civilization. Specialization also helps tackle the problem of the ever-increasing mass of information and knowledge.

The individual may tend to develop a sense of insecurity when exposed to the environment more complex than him/her. But the situation is mitigated by the fact that, since the entire system is one big complex system, this superorganism has the usual tendencies like the motivation to survive. This purpose is served better if the superorganism (namely the human civilization as a whole) attempts to protect and nourish its components, namely the human beings. An example is our better health and life-expectancy.

Saturday, 7 December 2013

109. Sociobiology, Altruism, Morality, Group Selection

A group comprising many individual agents working together can be viewed as a problem-solving system. Each agent may have some degree of autonomy, but may not be aware of the entire picture. This sort of teamwork is seen in many situations; e.g. in human sports and at the workplace, as also in social-insect colonies.

One of the great minds to have studied insect behaviour is E. O. Wilson, the Harvard naturalist. Wilson spent many decades decoding the biochemical communication mechanisms that ants use in order to function as a well-synched group. His discoveries led him to explore behaviour in all social organisms. He coined the word 'sociobiology', which is the study of the evolutionary basis for the behaviour of organisms. Wilson, in his 1975 book Sociobiology: The New Synthesis, took the scientific community by surprise with his assertion that biology also played a key role in human behaviour. At that time it was widely believed that human behaviour was purely culturally determined.

The triumph of Wilson's ideas was in presenting the self-organization view of group behaviour as a common aspect of both bee swarms and humans groups. The concept of the 'superorganism' was developed to express how a large group of non-intelligent agents can function as one highly intelligent entity. These entities can handle and synthesize large quantities of sensory data, and use the data to perform complex computations that are commonly associated with intelligence

Altruism and Natural Selection

Altruism can emerge in a species in spite of the fact that each individual is hard-wired to be selfish. It may appear at first sight that selfish individuals are more likely to survive and propagate their selfish-tendency genes. But if a group or a population as a whole has better survival chances if altruism prevails, than if rank selfishness prevails, altruism can emerge: Even though an altruist individual may not survive because it chooses to make sacrifices for the group, its altruistic genes will still survive in the population because the latter comprises of its brothers and sisters and other relatives. In such a situation, natural selection works in favour of promoting altruism in the gene pool.

In the human context, it should be clear to us that only a kind of collective altruism can ensure our survival as a species.

At what level does natural selection drive biological evolution? Is it all about selfish genes and fertile individuals, or can ‘group selection’ also occur? The group-selection idea involves altruistic behaviour conducive to the survival and propagation of a group as a whole, even at the cost of elimination of some individuals making the sacrifice for the sake of the group. Group selection is still a matter of debate, although it has been debunked by many experts.

Historically speaking, Darwin supported the idea of group selection (Mirsky 2009). He argued that, although moral men may not do better than immoral men at the level of the individual, tribes of moral men would ‘have an immense advantage’ compared to the survival and propagation rate of tribes with no moral scruples. But later opinion in the evolution community did not favour this postulate. The argument advanced was that at the genetic level it has to be ‘every man for himself’. I quote Steven Pinker: 'I am often asked whether I agree with the new group selectionists, and the questioners are always surprised when I say I do not. After all, group selection sounds like a reasonable extension of evolutionary theory and a plausible explanation of the social nature of humans. Also, the group selectionists tend to declare victory, and write as if their theory has already superseded a narrow, reductionist dogma that selection acts only at the level of genes. . . . The more carefully you think about group selection, the less sense it makes, and the more poorly it fits the facts of human psychology and history. . . . . Group selection has become a scientific dust bunny, a hairy blob in which anything having to do with "groups" clings to anything having to do with "selection." The problem with scientific dust bunnies is not just that they sow confusion; … the apparent plausibility of one restricted version of "group selection" often bleeds outwards to a motley collection of other, long-discredited versions. The problem is that it also obfuscates evolutionary theory by blurring genes, individuals, and groups as equivalent levels in a hierarchy of selectional units; ... this is not how natural selection, analyzed as a mechanistic process, really works. Most importantly, it has placed blinkers on psychological understanding by seducing many people into simply equating morality and culture with group selection, oblivious to alternatives that are theoretically deeper and empirically more realistic'.

Pinker summarizes his essay as follows: 'The idea of Group Selection has a superficial appeal because humans are indisputably adapted to group living and because some groups are indisputably larger, longer-lived, and more influential than others. This makes it easy to conclude that properties of human groups, or properties of the human mind, have been shaped by a process that is akin to natural selection acting on genes. Despite this allure, I have argued that the concept of Group Selection has no useful role to play in psychology or social science. It refers to too many things, most of which are not alternatives to the theory of gene-level selection but loose allusions to the importance of groups in human evolution. And when the concept is made more precise, it is torn by a dilemma. If it is meant to explain the cultural traits of successful groups, it adds nothing to conventional history and makes no precise use of the actual mechanism of natural selection. But if it is meant to explain the psychology of individuals, particularly an inclination for unconditional self-sacrifice to benefit a group of nonrelatives, it is dubious both in theory (since it is hard to see how it could evolve given the built-in advantage of protecting the self and one's kin) and in practice (since there is no evidence that humans have such a trait).

None of this prevents us from seeking to understand the evolution of social and moral intuitions, nor the dynamics of populations and networks which turn individual psychology into large-scale societal and historical phenomena. It's just that the notion of "group selection" is far more likely to confuse than to enlighten—especially as we try to understand the ideas and institutions that human cognition has devised to make up for the shortcomings of our evolved adaptations to group living'.

Kerry Koyen (2012) has also argued strongly against group selection.

And here is more from Pinker on morality: 'Nor is morality any mystery. Abstract, universal morality (e.g., a Kantian categorical imperative) never evolved in the first place, but took millennia of debate and cultural experience, and doesn’t characterize the vast majority of humanity. More rudimentary moral sentiments that may have evolved – sympathy, trust, retribution, gratitude, guilt – are stable strategies in cooperation games, and emerge in computer simulations'.