Recent Technological Advances in Natural Language Processing and Artificial Intelligence

TL;DR

This paper reviews recent advances in natural language processing and AI, focusing on projects like IBM's Jeopardy and WolframAlpha, and discusses their implications for passing the Turing test.

Contribution

It provides an overview of key AI projects and explores potential future developments to achieve human-like AI understanding.

Findings

IBM's Jeopardy demonstrates advanced question-answering capabilities.

WolframAlpha integrates computational knowledge for precise responses.

Implications suggest progress towards passing the Turing test by 2029.

Abstract

A recent advance in computer technology has permitted scientists to implement and test algorithms that were known from quite some time (or not) but which were computationally expensive. Two such projects are IBM's Jeopardy as a part of its DeepQA project [1] and Wolfram's Wolframalpha[2]. Both these methods implement natural language processing (another goal of AI scientists) and try to answer questions as asked by the user. Though the goal of the two projects is similar, both of them have a different procedure at it's core. In the following sections, the mechanism and history of IBM's Jeopardy and Wolfram alpha has been explained followed by the implications of these projects in realizing Ray Kurzweil's [3] dream of passing the Turing test by 2029. A recipe of taking the above projects to a new level is also explained.