Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices

PARSING STRUKTUR PARAGRAF BERBASIS NEURAL NETWORK

Parsing paragraf memiliki peran penting dalam perkembangan kecerdasan buatan. Parsing menjadi langkah awal untuk menalar paragraf agar bisa dimengerti oleh mesin. Keefektifan metode parsing paragraf bergantung pada bagaimana mendekomposisikan teks ke segmen teks. Proses segmentasi tanpa memperhitungkan struktur semantik dari suatu paragraf akan menghasilkan struktur yang tidak sinkron dengan makna sebenarnya. Untuk mengatasi masalah ini, penelitian ini mengusulkan penerapan metode berbasis recursive neural network (RvNN). Metode ini berupaya mendapatkan binary tree terbaik yang merepresentasikan struktur paragraf. Metode usulan diterapkan untuk menyelesaikan paragraf-paragraf sederhana yaitu soal cerita anak. Hasil uji coba menunjukkan bahwa metode usulan dapat memparsing paragraf dengan tingkat akurasi sebesar 0.9. Metode usulan juga lebih efisien karena tidak perlu membuat repositori kerangka struktu

The Taboo Challenge Competition

Games have always been a popular domain of AI research, and they have been used for many recent competitions. However, reaching human-level performance often either focuses on comprehensive world knowledge or solving decision-making problems with unmanageable solution spaces. Building on the popular Taboo board game, the Taboo Challenge Competition addresses a different problem — that of bridging the gap between the domain knowledge of heterogeneous agents trying to jointly identify a concept without making reference to its most salient features. The competition, which was run for the first time at IJCAI 2017, aims to provide a simple testbed for diversity-aware AI where the focus is on integrating independently engineered AI components, while offering a scenario that is challenging yet simple enough to not require mastering general commonsense knowledge or natural language understanding. We describe the design and preparation of the competition, discuss results, and lessons learned

Parsing struktur semantik soal cerita matematika berbahasa indonesia menggunakan recursive neural network

Soal cerita berperan penting untuk kemajuan pengembangan kecerdasan buatan. Hal ini karena penyelesaian soal cerita melibatkan pengembangan sebuah sistem yang mampu memahami bahasa alami. Pembentukan sistem penyelesaian soal memerlukan mekanisme untuk mendekomposisikan teks soal ke segmen-segmen teks untuk diterjemahkan ke jenis operasi hitung. Segmen-segmen tersebut ditentukan melalui proses parsing semantik struktur soal agar menghasilkan segmen-segmen yang maknanya menunjuk operasi hitung. Sejumlah metode usulan saat ini sesuai untuk diterapkan pada soal cerita berbahasa Inggris dan belum diterapkan pada soal cerita berbahasa Indonesia. Dampaknya adalah segmen-segmen yang dihasilkan belum tentu menghasilkan urutan pengerjaan operasi yang sesuai makna cerita. Penelitian ini mengusulkan penggunaaan Recursive Neural Network (RNN) sebagai parser struktur semantik soal cerita berbahasa Indonesia. Pengujian parser struktur semantik soal dilakukan terhadap soal-soal yang berasal dari Buku Sekolah Elektronik (BSE) Sekolah Dasar (SD) dari Pusat Perbukuan Kementerian Pendidikan dan Kebudayaan. Hasil pengujian menunjukkan akurasi akhir sebesar 86,4%.  Math word problems play an important role for the development of artificial intelligent. This is because solving word problems involves the development of a system that can understand natural language.  Designing a system for solving math word problems requires a mechanism for decomposing a text into segments of text to be translated into math operation. The segments are categorized through the process of parsing the semantic structure of the word problems to obtain segments whose meanings refer to math operation. A number of current proposed methods are suitable to be applied to English math word problems and have never been applied to Indonesian math word problems. The impact is that the segments produced are not necessarily in line with the sequences of operations appropriate with the meaning of the story.  This study proposed the use of Recursive Neural Network (RNN) as a parser of semantic structure of Indonesian math word problems. The testing of the parser was carried out on the math word problems taken from the Elementary School’s Electronic School Book  (BSE) published by the Book Center of the Ministry of Education and Culture. The result of the testing showed that the final accuracy was 86.4%