Topic
Turing
About: Turing is a research topic. Over the lifetime, 2877 publications have been published within this topic receiving 52642 citations.
Papers published on a yearly basis
1 / 2
Papers
[...]
1 Jan 2003
TL;DR: 'Without symbolism the life of man would be like that of the prisoners in the cave of Plato's simile; it could find no access to the "ideal world" which is opened to him from different sides by religion, art, philosophy, science.
Abstract: 'Without symbolism the life of man would be like that of the prisoners in the cave of Plato's simile…confined within the limits of his biological needs and practical interests; it could find no access to the "ideal world" which is opened to him from different sides by religion, art, philosophy, science.' Ernst Cassirer 1 ABSTRACT I begin by outlining some of the positions that have been taken by those who have reflected upon the nature of language. In his early work Wittgenstein asserts that language becomes meaningful when we tacitly adhere to the rules of logic. In his later work he claims that lan- guages become meaningful when they are situated within forms of life. Polanyi describes language as a toolbox for deploying our tacit awareness. A meaning is generated when a point of view attends from a subsidiary to a focal awareness. Languages re-present these meanings. Although all languages rely upon rules, what it is to be a meaning is not reduc- ible to rules. Nor is there a universal grammar. Because it renders abstract reflection possi- ble, language renders minds possible. A mind is not the product of an innate language of thought; it is a consequence of indwelling within a natural language. Indwelling within languages enables us to access new realities. Languages however do not supply us with the boundaries of the world. Not only do we know more than we can say, we can also say more than we know. The ultimate context of our linguistic meanings is not our social practices; it is our embodied awareness of the world. A representationalist account is in accordance with the view that minds are Turing machines. But the symbols processed by a Turing ma- chine derive their meaning from the agents that use them to achieve their purposes. Only if the processing of symbolic representations is related to the tacit context within which they become meaningful, does a semantic engine becomes possible.
5,233 citations
TL;DR: A computational model is described that learns in a similar fashion and does so better than current deep learning algorithms and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce.
Abstract: People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.
3,258 citations
TL;DR: This work experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback and implements a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities.
Abstract: Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.
908 citations
Book•
8 Sep 1996
TL;DR: The potentialities and limitations of computing machines were discussed in a course at Caltech called "Potentialities and Limitations of Computing Machines" as mentioned in this paper, where the authors present a "Feynmanesque" overview of standard and some not-so-standard topics in computer science.
Abstract: From the Publisher:
From 1983 to 1986, the legendary physicist and teacher Richard Feynman gave a course at Caltech called "Potentialities and Limitations of Computing Machines." Although the lectures are over ten years old, most of the material is timeless and presents a "Feynmanesque" overview of many standard and some not-so-standard topics in computer science. These include compatibility, Turing machines (or as Feynman said, "Mr. Turing's machines"), information theory, Shannon's Theorem, reversible computation, the thermodynamics of computation, the quantum limits to computation, and the physics of VLSI devices. Taken together, these lectures represent a unique exploration of the fundamental limitations of digital computers. Feynman's philosophy of learning and discovery comes through strongly in these lectures. He constantly points out the benefits of playing around with concepts and working out solutions to problems on your own - before looking at the back of the book for the answers. As Feynman says in the lectures: "If you keep proving stuff that others have done, getting confidence, increasing the complexities of your solutions - for the fun of it - then one day you'll turn around and discover that nobody actually did that one! And that's the way to become a computer scientist."
687 citations
Posted Content•
TL;DR: This paper proposed using adversarial training for open-domain dialogue generation, where the generator is trained to generate sequences that are indistinguishable from human-generated dialogue utterances, and the outputs from the discriminator are used as rewards for the generator.
Abstract: In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator---analagous to the human evaluator in the Turing test--- to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues.
In addition to adversarial training we describe a model for adversarial {\em evaluation} that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.
651 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2026 | 6 |
| 2025 | 184 |
| 2024 | 226 |
| 2023 | 317 |
| 2022 | 433 |
| 2021 | 157 |