Very good article by Matteo Pasquinelli — I’ve excerpted a lot of it below, but read the whole thing! — Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference
The term Artificial Intelligence is often cited in popular press as well as in art and philosophy circles as an alchemic talisman whose functioning is rarely explained. The hegemonic paradigm to date (also crucial to the automation of labor) is not based on GOFAI (Good Old-Fashioned Artificial Intelligence that never succeeded at automating symbolic deduction), but on the neural networks designed by Frank Rosenblatt back in 1958 to automate statistical induction. The text highlights the role of logic gates in the distributed architecture of neural networks, in which a generalized control loop affects each node of computation to perform pattern recognition. In this distributed and adaptive architecture of logic gates, rather than applying logic to information top-down, information turns into logic, that is, a representation of the world becomes a new function in the same world description. This basic formulation is suggested as a more accurate definition of learning to challenge the idealistic definition of (artificial) intelligence. If pattern recognition via statistical induction is the most accurate descriptor of what is popularly termed Artificial Intelligence, the distorting effects of statistical induction on collective perception, intelligence and governance (over-fitting, apophenia, algorithmic bias, “deep dreaming,” etc.) are yet to be fully understood
Any machine is always a machine of cognition, a product of the human intellect and unruly component of the gears of extended cognition. Thanks to machines, the human intellect crosses new landscapes of logic in a materialistic way—that is, under the influence of historical artifacts rather than Idealism. As, for instance, the thermal engine prompted the science of thermodynamics (rather than the other way around), computing machines can be expected to cast a new light on the philosophy of the mind and logic itself. When Alan Turing came up with the idea of a universal computing machine, he aimed at the simplest machination to calculate all possible functions. The efficiency of the universal computer catalyzed in Turing the alchemic project for the automation of human intelligence. However, it would be a sweet paradox to see the Turing machine that was born as Gedankenexperiment to demonstrate the incompleteness of mathematics aspiring to describe an exhaustive paradigm of intelligence (as the Turing test is often understood).
Within neural networks (as according also to the classical cybernetic framework), information becomes control; that is, a numerical input retrieved from the world turns into a control function of the same world. More philosophically, it means that a representation of the world (information) becomes a new rule in the same world (function), yet under a good degree of statistical approximation. Information becoming logic is a very crude formulation of intelligence, which however aims to stress openness to the world as a continuous process of learning.
Rosenblatt stressed that artificial neural networks are both a simplification and exaggeration of nervous systems and this approximation (that is the recognition of limits in model-based thinking) should be a guideline for any philosophy of the (artefactual) mind. Ultimately Rosenblatt proposed neurodynamics as a discipline against the hype of artificial intelligence.
The perceptron program is not primarily concerned with the invention of devices for “artificial intelligence”, but rather with investigating the physical structures and neurodynamic principles which underlie “natural intelligence.” A perceptron is first and foremost a brain model, not an invention for pattern recognition. As a brain model, its utility is in enabling us to determine the physical conditions for the emergence of various psychological properties. It is by no means a “complete” model, and we are fully aware of the simplifications which have been made from biological systems; but it is, at least, as analyzable model.
In 1969 Marvin Minsky and Seymour Papert’s book, titled Perceptrons, attacked Rosenblatt’s neural network model by wrongly claiming that a Perceptron (although a simple single-layer one) could not learn the XOR function and solve classifications in higher dimensions. This recalcitrant book had a devastating impact, also because of Rosenblatt’s premature death in 1971, and blocked funds to neural network research for decades. What is termed as the first ‘winter of Artificial Intelligence’ would be better described as the ‘winter of neural networks,’ which lasted until 1986 when the two volumes Parallel Distributed Processing clarified that (multilayer) Perceptrons can actually learn complex logic functions. Half a century and many more neurons later, pace Minsky, Papert and the fundamentalists of symbolic AI, multilayer Perceptrons are capable of better-than-human image recognition, and they constitute the core of Deep Learning systems such as automatic translation and self-driving cars.
Rosenblatt gave probably one of the first descriptions of machine intelligence as emergent property: “It is significant that the individual elements, or cells, of a nerve network have never been demonstrated to possess any specifically psychological functions, such as ‘memory,’ ‘awareness,’ or ‘intelligence.’ Such properties, therefore, presumably reside in the organization and functioning of the network as a whole, rather than in its elementary parts.” [Gestalt overtones here]
Current techniques of Artificial Intelligence are clearly a sophisticated form of pattern recognition rather than intelligence, if intelligence is understood as the discovery and invention of new rules. To be precise in terms of logic, what neural networks calculate is a form of statistical induction. Of course, such an extraordinary form of automated inference can be a precious ally for human creativity and science (and it is the closest approximation to what is known as Peirce’s weak abduction), but it does not represent per se the automation of intelligence qua invention, precisely as it remains within ‘too human’ categories.
Peirce said that “man is an external sign.” If this intuition encouraged philosophers to stress that the human mind is an artifactual project that extends into technology, however, the human mind’s actual imbrication with the external machines of cognition happened to be rarely empirically illustrated. This has produced simplistic poses in which ideas such as Artificial General Intelligence and Superintelligence are evoked as alchemic talismans of posthumanism with little explanation of the inner workings and postulates of computation. A fascinating aspect of neural computation is actually the way it amplifies the categories of human knowledge rather than supersedes them in autonomous forms. Contrary to the naïve conception of the autonomy of artificial intelligence, in the architecture of neural networks many elements are still deeply affected by human intervention. If one wants to understand how much neural computation extends into the ‘inhuman,’ one should discern how much it is still ‘too human.’
The issue of over-fitting points to a more fundamental issue in the constitution of the training dataset: the boundary of the categories within which the neural network operates. The way a training dataset represents a sample of the world marks, at the same time, a closed universe. What is the relation of such a closed data universe with the outside? A neural network is considered ‘trained’ when it is able to generalize its results to unknown data with a very low margin of error, yet such a generalization is possible due to the homogeneity between training and test dataset. A neural network is never asked to perform across categories that do not belong to its ‘education.’ The question is then: How much is a neural network (and AI in general) capable of escaping the categorical ontology in which it operates?
Charles S. Peirce’s distinction between deduction, induction and abduction (hypothesis) is the best way to frame the limits and potentialities of machine intelligence. Peirce remarkably noticed that the classic logical forms of inference—deduction and induction—never invent new ideas but just repeat quantitative facts. Only abduction (hypothesis) is capable of breaking into new worldviews and inventing new rules.
The only thing that induction accomplishes is to determine the value of a quantity. It sets out with a theory and it measures the degree of concordance of that theory with fact. It never can originate any idea whatever. No more can deduction. All the ideas of science come to it by the way of Abduction. Abduction consists in studying facts and devising a theory to explain them.
Specifically, Peirce’s distinction between abduction and induction can illuminate the logic form of neural networks, as since their invention by Rosemblaat they were designed to automate complex forms of induction.
By induction, we conclude that facts, similar to observed facts, are true in cases not examined. By hypothesis, we conclude the existence of a fact quite different from anything observed, from which, according to known laws, something observed would necessary result. The former, is reasoning from particulars to the general law; the latter, from effect to cause. The former classifies, the latter explains.
The distinction between induction as classifier and abduction as explainer frames very well also the nature of the results of neural networks (and the core problem of Artificial Intelligence). The complex statistical induction that is performed by neural networks gets close to a form of weak abduction, where new categories and ideas loom on the horizon, but it appears invention and creativity are far from being fully automated. The invention of new rules (an acceptable definition of intelligence) is not just a matter of generalization of a specific rule (as in the case of induction and weak abduction) but of breaking through semiotic planes that were not connected or conceivable beforehand, as in scientific discoveries or the creation of metaphors (strong abduction).
In his critique of artificial intelligence, Umberto Eco remarked: “No algorithm exists for the metaphor, nor can a metaphor be produced by means of a computer’s precise instructions, no matter what the volume of organized information to be fed in.” Eco stressed that algorithms are not able to escape the straitjacket of the categories that are implicitly or explicitly embodied by the “organized information” of the dataset. Inventing a new metaphor is about making a leap and connecting categories that never happened to be logically related. Breaking a linguistic rule is the invention of a new rule, only when it encompasses the creation of a more complex order in which the old rule appears as a simplified and primitive case. Neural networks can a posteriori compute metaphors but cannot a priori automate the invention of new metaphors (without falling into comic results such as random text generation). The automation of (strong) abduction remains the philosopher’s stone of Artificial Intelligence.
Statistical inference via neural networks has enabled computational capitalism to imitate and automate both low and hi-skill labor.Nobody expected that even a bus driver could become a source of cognitive labor to be automated by neural networks in self-driving vehicles. Automation of intelligence via statistical inference is the new eye that capital casts on the data ocean of global labor, logistics, and markets with novel effects of abnormalization—that is, distortion of collective perception and social representations, as it happens in the algorithmic magnification of class, race and gender bias. Statistical inference is the distorted, new eye of the capital’s Master.