Pain and dying. It’s what humans do. We endure the pain of birth through a passage that is too narrow both for child and mother. In China, nearly 37 per cent of birth are done through Caesarean sections. We also use pain killers. Could the natural birthing limit the size of our brain? Much of what we do in life is designed to alleviate pain and achieve a certain level of contentment, though we are often told “no pain, no gain”. This of course is a contentious concept. Can we achieve a better way of thinking? We vie to be relatively happy....

In eighteenth century Europe, a bold new idea came along with the “robots” of Monsieur Jacques de Vaucanson. They were not robots per say, but automated figurines that through various clock mechanisms could perform complex tasks such as playing music, drawing images and playing games. Eventually some people thought that such machines could develop reason and emotions.

It took many more years before this possibility was within reach. Now we are exploring and creating Artificial Intelligence — beyond the comfort of traditional thinking which for many centuries was the domain of religions and other controlling beliefs. Sciences have now taken over most of human intellectual endeavours. AI, or Artificial Intelligence, is a frightening concept to people who still like to think with an obsolete religious morality — or fear for the relevance of humanity. Can we deal with this, by spurring scientific interest in young developing minds and developing scientific wonderment in adults, beyond Apps?

Since the beginning of the 20th century, superior intelligence depiction and exploration has been the domain of science fiction….

Now, the AI “machines” are not born through pain and may not encounter death, nor they may have any emotions, but may have far more reason than us… They consume vast amount of energy in the form of electricity, and, as well as "human input" they basically “communicate” via a set up of connections, including the internet — and a web of radio/light-waves… Can they think better than us? Can they think better and DIFFERENTLY will be the main questions…

In 2012, Noam Chomsky was not impressed....

An extended conversation with the legendary linguist

If one were to rank a list of civilization's greatest and most elusive intellectual challenges, the problem of "decoding" ourselves—understanding the inner workings of our minds and our brains, and how the architecture of these elements is encoded in our genome—would surely be at the top. Yet the diverse fields that took on this challenge, from philosophy and psychology to computer science and neuroscience, have been fraught with disagreement about the right approach.

In 1956, the computer scientist John McCarthy coined the term "Artificial Intelligence" (AI) to describe the study of intelligence by implementing its essential features on a computer. Instantiating an intelligent system using man-made hardware, rather than our own "biological hardware" of cells and tissues, would show ultimate understanding, and have obvious practical applications in the creation of intelligent devices or even robots.

Some of McCarthy's colleagues in neighboring departments, however, were more interested in how intelligence is implemented in humans (and other animals) first. Noam Chomsky and others worked on what became cognitive science, a field aimed at uncovering the mental representations and rules that underlie our perceptual and cognitive abilities. Chomsky and his colleagues had to overthrow the then-dominant paradigm of behaviorism, championed by Harvard psychologist B.F. Skinner, where animal behavior was reduced to a simple set of associations between an action and its subsequent reward or punishment. The undoing of Skinner's grip on psychology is commonly marked by Chomsky's 1959 critical review of Skinner's book Verbal Behavior, a book in which Skinner attempted to explain linguistic ability using behaviorist principles.

Skinner's approach stressed the historical associations between a stimulus and the animal's response—an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past. Chomsky's conception of language, on the other hand, stressed the complexity of internal representations, encoded in the genome, and their maturation in light of the right data into a sophisticated computational system, one that cannot be usefully broken down into a set of associations. Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment. The "language faculty," as Chomsky referred to it, was part of the organism's genetic endowment, much like the visual system, the immune system and the circulatory system, and we ought to approach it just as we approach these other more down-to-earth biological systems.

David Marr, a neuroscientist colleague of Chomsky's at MIT, defined a general framework for studying complex biological systems (like the brain) in his influential book Vision, one that Chomsky's analysis of the language capacity more or less fits into. According to Marr, a complex biological system can be understood at three distinct levels. The first level ("computational level") describes the input and output to the system, which define the task the system is performing. In the case of the visual system, the input might be the image projected on our retina and the output might our brain's identification of the objects present in the image we had observed. The second level ("algorithmic level") describes the procedure by which an input is converted to an output, i.e. how the image on our retina can be processed to achieve the task described by the computational level. Finally, the third level ("implementation level") describes how our own biological hardware of cells implements the procedure described by the algorithmic level.

The approach taken by Chomsky and Marr toward understanding how our minds achieve what they do is as different as can be from behaviorism. The emphasis here is on the internal structure of the system that enables it to perform a task, rather than on external association between past behavior of the system and the environment. The goal is to dig into the "black box" that drives the system and describe its inner workings, much like how a computer scientist would explain how a cleverly designed piece of software works and how it can be executed on a desktop computer.

As written today, the history of cognitive science is a story of the unequivocal triumph of an essentially Chomskyian approach over Skinner's behaviorist paradigm—an achievement commonly referred to as the "cognitive revolution," though Chomsky himself rejects this term. While this may be a relatively accurate depiction in cognitive science and psychology, behaviorist thinking is far from dead in related disciplines. Behaviorist experimental paradigms and associationist explanations for animal behavior are used routinely by neuroscientists who aim to study the neurobiology of behavior in laboratory animals such as rodents, where the systematic three-level framework advocated by Marr is not applied.

In May of last year, during the 150th anniversary of the Massachusetts Institute of Technology, a symposium on "Brains, Minds and Machines" took place, where leading computer scientists, psychologists and neuroscientists gathered to discuss the past and future of artificial intelligence and its connection to the neurosciences.

The gathering was meant to inspire multidisciplinary enthusiasm for the revival of the scientific question from which the field of artificial intelligence originated: How does intelligence work? How does our brain give rise to our cognitive abilities, and could this ever be implemented in a machine?

Noam Chomsky, speaking in the symposium, wasn't so enthused. Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI"—focused on using statistical learning techniques to better mine and predict data— is unlikely to yield general principles about the nature of intelligent beings or about cognition.

This critique sparked an elaborate reply to Chomsky from Google's director of research and noted AI researcher, Peter Norvig, who defended the use of statistical models and argued that AI's new methods and definition of progress is not far off from what happens in the other sciences.

Chomsky acknowledged that the statistical approach might have practical value, just as in the example of a useful search engine, and is enabled by the advent of fast computers capable of processing massive data. But as far as a science goes, Chomsky would argue it is inadequate, or more harshly, kind of shallow. We wouldn't have taught the computer much about what the phrase "physicist Sir Isaac Newton" really means, even if we can build a search engine that returns sensible hits to users who type the phrase in.

It turns out that related disagreements have been pressing biologists who try to understand more traditional biological systems of the sort Chomsky likened to the language faculty. Just as the computing revolution enabled the massive data analysis that fuels the "new AI," so has the sequencing revolution in modern biology given rise to the blooming fields of genomics and systems biology. High-throughput sequencing, a technique by which millions of DNA molecules can be read quickly and cheaply, turned the sequencing of a genome from a decade-long expensive venture to an affordable, commonplace laboratory procedure. Rather than painstakingly studying genes in isolation, we can now observe the behavior of a system of genes acting in cells as a whole, in hundreds or thousands of different conditions.

The sequencing revolution has just begun and a staggering amount of data has already been obtained, bringing with it much promise and hype for new therapeutics and diagnoses for human disease. For example, when a conventional cancer drug fails to work for a group of patients, the answer might lie in the genome of the patients, which might have a special property that prevents the drug from acting. With enough data comparing the relevant features of genomes from these cancer patients and the right control groups, custom-made drugs might be discovered, leading to a kind of "personalized medicine." Implicit in this endeavor is the assumption that with enough sophisticated statistical tools and a large enough collection of data, signals of interest can be weeded it out from the noise in large and poorly understood biological systems.

The success of fields like personalized medicine and other offshoots of the sequencing revolution and the systems-biology approach hinge upon our ability to deal with what Chomsky called "masses of unanalyzed data"—placing biology in the center of a debate similar to the one taking place in psychology and artificial intelligence since the 1960s.

Systems biology did not rise without skepticism. The great geneticist and Nobel-prize winning biologist Sydney Brenner once defined the field as "low input, high throughput, no output science." Brenner, a contemporary of Chomsky who also participated in the same symposium on AI, was equally skeptical about new systems approaches to understanding the brain. When describing an up-and-coming systems approach to mapping brain circuits called Connectomics, which seeks to map the wiring of all neurons in the brain (i.e. diagramming which nerve cells are connected to others), Brenner called it a "form of insanity."

Brenner's catch-phrase bite at systems biology and related techniques in neuroscience is not far off from Chomsky's criticism of AI. An unlikely pair, systems biology and artificial intelligence both face the same fundamental task of reverse-engineering a highly complex system whose inner workings are largely a mystery. Yet, ever-improving technologies yield massive data related to the system, only a fraction of which might be relevant. Do we rely on powerful computing and statistical approaches to tease apart signal from noise, or do we look for the more basic principles that underlie the system and explain its essence? The urge to gather more data is irresistible, though it's not always clear what theoretical framework these data might fit into. These debates raise an old and general question in the philosophy of science: What makes a satisfying scientific theory or explanation, and how ought success be defined for science?

I sat with Noam Chomsky on an April afternoon in a somewhat disheveled conference room, tucked in a hidden corner of Frank Gehry's dazzling Stata Center at MIT. I wanted to better understand Chomsky's critique of artificial intelligence and why it may be headed in the wrong direction. I also wanted to explore the implications of this critique for other branches of science, such neuroscience and systems biology, which all face the challenge of reverse-engineering complex systems—and where researchers often find themselves in an ever-expanding sea of massive data. The motivation for the interview was in part that Chomsky is rarely asked about scientific topics nowadays. Journalists are too occupied with getting his views on U.S. foreign policy, the Middle East, the Obama administration and other standard topics. Another reason was that Chomsky belongs to a rare and special breed of intellectuals, one that is quickly becoming extinct. Ever since Isaiah Berlin's famous essay, it has become a favorite pastime of academics to place various thinkers and scientists on the "Hedgehog-Fox" continuum: the Hedgehog, a meticulous and specialized worker, driven by incremental progress in a clearly defined field versus the Fox, a flashier, ideas-driven thinker who jumps from question to question, ignoring field boundaries and applying his or her skills where they seem applicable. Chomsky is special because he makes this distinction seem like a tired old cliche. Chomsky's depth doesn't come at the expense of versatility or breadth, yet for the most part, he devoted his entire scientific career to the study of defined topics in linguistics and cognitive science. Chomsky's work has had tremendous influence on a variety of fields outside his own, including computer science and philosophy, and he has not shied away from discussing and critiquing the influence of these ideas, making him a particularly interesting person to interview. Videos of the interview can be found here.

https://www.theatlantic.com/technology/archive/2012/11/noam-chomsky-on-where-artificial-intelligence-went-wrong/261637/

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