117,208 research outputs found
Zombies in Searle's Chinese Room: Putting the Turing Test to Bed
Searle’s discussions over the years 1980-2004 of the implications of his “Chinese Room” Gedanken experiment are frustrating because they proceed from a correct assertion: (1) “Instantiating a computer program is never by itself a sufficient condition of intentionality;” and an incorrect assertion: (2) “The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program.” In this article, I describe how to construct a Gedanken zombie Chinese Room program that will pass the Turing test and at the same time unambiguously demonstrates the correctness of (1). I then describe how to construct a Gedanken Chinese brain program that will pass the Turing test, has a mind, and understands Chinese, thus demonstrating that (2) is incorrect. Searle’s instantiation of this program can and does produce intentionality. Searle’s longstanding ignorance of Chinese is simply irrelevant and always has been. I propose a truce and a plan for further exploration
Empirical Encounters with Computational Irreducibility and Unpredictability
There are several forms of irreducibility in computing systems, ranging from
undecidability to intractability to nonlinearity. This paper is an exploration
of the conceptual issues that have arisen in the course of investigating
speed-up and slowdown phenomena in small Turing machines. We present the
results of a test that may spur experimental approaches to the notion of
computational irreducibility. The test involves a systematic attempt to outrun
the computation of a large number of small Turing machines (all 3 and 4 state,
2 symbol) by means of integer sequence prediction using a specialized function
finder program. This massive experiment prompts an investigation into rates of
convergence of decision procedures and the decidability of sets in addition to
a discussion of the (un)predictability of deterministic computing systems in
practice. We think this investigation constitutes a novel approach to the
discussion of an epistemological question in the context of a computer
simulation, and thus represents an interesting exploration at the boundary
between philosophical concerns and computational experiments.Comment: 18 pages, 4 figure
Pengembangan Media Pembelajaran Bahasa Isyarat untuk Panduan Turing dengan Metode Pd pada Paguyuban Vixion Owners Semarang
Media video pembelajaran adalah media yang menyajikan audio dan visual yang berisi pesan-pesan pembelajaran untuk membantu pemahaman terhadap suatu materi pembelajaran. Video merupakan bahan pembelajaran tampak-dengar (audio-visual) kerena unsur dengar (audio) dan unsur visual/video (tampak) dapat disajikan serentak. Video pembelajaran sebagai bahan ajar bertujuan memperjelas dan mempermudah pesan agar tidak terlalu verbaltis, mengatasi keterbatasan waktu, ruang dan daya indra peserta maupun pengajar. Vedeo pembelajaran inilah yang akan digunakan di Vixion Owners Semarang (VIOS) sebagai media pembelajaran bahasa isyarat untuk panduan turing. Tujuan penelitian ini adalah untuk: 1. Mendapatkan video pembelajaran bahasa isyarat panduan turing yang valid untuk diterapkan di VIOS, 2. Penerapan video pembelajaran bahasa isyarat untuk panduan turing yang efektif pada VIOS, sehingga mudah dimengerti dan dipahami oleh setiap anggota. Sehingga tercipta turing yang lancar, aman, nyaman dan menyenangkan, serta semua kegiatan turing berjalan sesuai dengan waktu yang ditentukan. Penelitian ini adalah jenis penelitian dan pengembangan atau dikenal dengan istilah Research and Development (R & D). Dengan langkah-langkah yang dilakukan, yaitu: (1) Studi pendahuluan: Kajian teori dan survai awal di VIOS, profil, data dan permasalahan. (2) Perencanaan penelitian: Merumuskan tujuan dan gagasan pengembangan (3) Pengembangan produk awal: Menentukan desain produk sampai dengan memproduksi video. (4) Uji lapangan terbatas (preliminary field test): Validasi produk, mengetahui kelayakan desain produk (5) Revisi hasil uji lapangan terbatas: Mendapatkan desain produk yang lebih baik (6) Uji lapangan lebih luas (main field test): Mendapatkan produk video pembelajaran yang layak digunakan di VIOS. Sedangkan metode pembelajaran yang digunakan dalam penelitian ini adalah Metode Demontrasi dan Metode Peer Teaching Menthods (teman mengajari teman). Gabungan dari kedua metode ini menghasilkan metode baru yang di sebut Metode PD. Media video yang dikembangkan ini dikatakan valid apabila mencapai nilai >60 % - 79% (cukup layak) dan >80% -100% (layak). Jika kreteria nilai tersebut tercapai, media video ini dapat dimanfaatkan sebagai media pembelajaran. Dari hasil pengujian validasi produk untuk media video pembelajaran diperoleh , kelayakan menurut ahli media 87,5 %, ahli materi 90 % dan hasil uji lapangan (user) rata-rata 91 %. Berdasarkan kreteria yang sudah ditentukan dapat dikatakan bahwa media video pembelajaran bahasa isyarat untuk panduan turing dengan metode PD yang dikemas dalam bentuk CD interaktif ini memenuhi kriteria 80 % - 100 % termasuk dalam kategori layak, sehingga media ini dapat digunakan pada Paguyuban Vixion Owners Semarang
The Turing Deception
This research revisits the classic Turing test and compares recent large
language models such as ChatGPT for their abilities to reproduce human-level
comprehension and compelling text generation. Two task challenges --
summarization, and question answering -- prompt ChatGPT to produce original
content (98-99%) from a single text entry and also sequential questions
originally posed by Turing in 1950. We score the original and generated content
against the OpenAI GPT-2 Output Detector from 2019, and establish multiple
cases where the generated content proves original and undetectable (98%). The
question of a machine fooling a human judge recedes in this work relative to
the question of "how would one prove it?" The original contribution of the work
presents a metric and simple grammatical set for understanding the writing
mechanics of chatbots in evaluating their readability and statistical clarity,
engagement, delivery, and overall quality. While Turing's original prose scores
at least 14% below the machine-generated output, the question of whether an
algorithm displays hints of Turing's truly original thoughts (the "Lovelace
2.0" test) remains unanswered and potentially unanswerable for now
E-CAPTCHA: A Two Way Graphical Password based Hard AI Problem
CAPTCHA is a Turing test that people can succeed, however current PC program could not succeed. The primary motivation behind CAPTCHA is to restrict automated scripts that are posted spam content. To upgrade the security another system Enhanced-CAPTCHA(E-CAPTCHA) is going to develop which includes some new elements specifically the Novel security based Grid-Box method where high security can accomplished by including 2 level of accessing
Characterizing the easy-to-find subgraphs from the viewpoint of polynomial-time algorithms, kernels, and Turing kernels
We study two fundamental problems related to finding subgraphs: (1) given graphs G and H, Subgraph Test asks if H is isomorphic to a subgraph of G, (2) given graphs G, H, and an integer t, PACKING asks if G contains t vertex-disjoint subgraphs isomorphic to H. For every graph class F, let F-Subgraph Test and F-Packing be the special cases of the two problems where H is restricted to be in F. Our goal is to study which classes F make the two problems tractable in one of the following senses:
- (randomized) polynomial-time solvable,
- admits a polynomial (many-one) kernel (that is, has a polynomial-time preprocessing procedure that creates an equivalent instance whose size is polynomially bounded by the size of the solution), or
- admits a polynomial Turing kernel (that is, has an adaptive polynomial-time procedure that reduces the problem to a polynomial number of instances, each of which has size bounded polynomially by the size of the solution).
To obtain a more robust setting, we restrict our attention to hereditary classes F.
It is known that if every component of every graph in F has at most two vertices, then F-Packing is polynomial-time solvable, and NP-hard otherwise. We identify a simple combinatorial property (every component of every graph in F either has bounded size or is a bipartite graph with one of the sides having bounded size) such that if a hereditary class F has this property, then F-Packing admits a polynomial kernel, and has no polynomial (many-one) kernel otherwise, unless the polynomial hierarchy collapses. Furthermore, if F does not have this property, then F-Packing is either WK[1]-hard, W[1]-hard, or Long Path-hard, giving evidence that it does not admit polynomial Turing kernels either.
For F-Subgraph Test, we show that if every graph of a hereditary class F satisfies the property that it is possible to delete a bounded number of vertices such that every remaining component has size at most two, then F-Subgraph Test is solvable in randomized polynomial time and it is NP-hard otherwise. We introduce a combinatorial property called (a, b, c, d)-splittability and show that if every graph in a hereditary class F has this property, then F-Subgraph Test admits a polynomial Turing kernel and it is WK[1]-hard, W[1]-hard, or Long Path-hard otherwise. We do not give a complete characterization of the cases when F-Subgraph Test admits polynomial many-one kernels, but show examples that this question is much more fragile than the characterization for Turing kernels
Characterizing the easy-to-find subgraphs from the viewpoint of polynomial-time algorithms, kernels, and Turing kernels
We study two fundamental problems related to finding subgraphs: (1) given
graphs G and H, Subgraph Test asks if H is isomorphic to a subgraph of G, (2)
given graphs G, H, and an integer t, Packing asks if G contains t
vertex-disjoint subgraphs isomorphic to H. For every graph class F, let
F-Subgraph Test and F-Packing be the special cases of the two problems where H
is restricted to be in F. Our goal is to study which classes F make the two
problems tractable in one of the following senses:
* (randomized) polynomial-time solvable,
* admits a polynomial (many-one) kernel, or
* admits a polynomial Turing kernel (that is, has an adaptive polynomial-time
procedure that reduces the problem to a polynomial number of instances, each of
which has size bounded polynomially by the size of the solution).
We identify a simple combinatorial property such that if a hereditary class F
has this property, then F-Packing admits a polynomial kernel, and has no
polynomial (many-one) kernel otherwise, unless the polynomial hierarchy
collapses. Furthermore, if F does not have this property, then F-Packing is
either WK[1]-hard, W[1]-hard, or Long Path-hard, giving evidence that it does
not admit polynomial Turing kernels either.
For F-Subgraph Test, we show that if every graph of a hereditary class F
satisfies the property that it is possible to delete a bounded number of
vertices such that every remaining component has size at most two, then
F-Subgraph Test is solvable in randomized polynomial time and it is NP-hard
otherwise. We introduce a combinatorial property called (a,b,c,d)-splittability
and show that if every graph in a hereditary class F has this property, then
F-Subgraph Test admits a polynomial Turing kernel and it is WK[1]-hard,
W[1]-hard, or Long Path-hard, otherwise.Comment: 69 pages, extended abstract to appear in the proceedings of SODA 201
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