Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

/pdf/whatever-next-predictive-brains-situated-agents-and-the-epj56cs868.pdf

Whatever next? Predictive brains, situated agents, and the future of cognitive science

The organization of behavior: A neuropsychological theory

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Spiking neural networks with spike-timing dependent plasticity (STDP) can learn to avoid the external stimulations spontaneously. This principle is called "Learning by Stimulation Avoidance" (LSA) ...

Learning by stimulation avoidance scales to large neural networks

Social network services (SNSs) are examples of non-living systems that evolve in response to internal and external events and have many similar characteristics assumed in biological evolution. In t...

Selection and Accelerated Divergence in Hashtag Evolution on a Social Network Service

The mating song of the male Bengalese finch can be described by a finite-state grammar and has the feature that more complex songs are preferred by females [1]-[3]. These facts suggest that complex song grammars may have evolved via sexual selection. How, then, do the female birds gauge a song’s complexity? Assuming that they can measure the complexity of a song while communicating with a male, but without making a model of the song, we studied the evolution of song grammars. In our simulation, it was demonstrated that song grammars became more complex through communication between coevolving males and females. Furthermore, when singing and listening were subject to fluctuations, peculiar features were observed in communication and evolution.

https://sacral.c.u-tokyo.ac.jp/pdf/sasaECAL95.pdf

Coevolution of Birdsong Grammar without Imitation

A series of simulation studies (Ono & Ikegami 1999; 2001) show that a proto cell spontaneously emerges from a chemical soup by acquiring membrane structures. In 2-dimensional space, the emergence of proto cells is followed by the reproduction of cells. A major unsolved problem is the evolution of proto cells; how the proto cells evolve into modern cells with higher functionalities. Here we examine, as the first step, the evolution of catalysts within the proto-cells. Catalytic chemicals have different catalytic activity in generating membrane chemicals. We show that cells with higher activity of membrane production evolve through cellular selection.

Selection of catalysts through cellular reproduction

Spacial Representation for Artificial Chemistry Based on Small-World Networks

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