To find if current dialogue systems use the same, psychotherapist questioning technique as Joseph Weizenbaum's 1960 natural language understanding programme, Eliza, the authors carried out an original experiment comparing five successful artificial dialogue systems, Cleverbot, Elbot, Eugene Goostman, JFred and Ultra Hal with an online version of Eliza. More than one hundred male and female participants with 1st or non-1st English language, age range 13–64, interacted with the systems over the Internet scoring each for conversation ability. Developers of the modern conversation systems show they deploy a variety of techniques to initiate and maintain dialogue learning from interactions with humans over the Internet. Statistical significance shows these dialogue systems are an improvement on their predecessor. Embedded on the web affording round-the-clock interaction the nature of artificial dialogue systems is evolving as these systems learn from the way humans converse. The uses of modern Elizas are proven successful as virtual assistants in e-commerce; their conversational basis is already extending into education. What we can say is modern artificial dialogue systems do talk. They are able to participate in conversation in a way their predecessor Eliza could not: they are able to share personal opinions, relay experience of family dramas, be relevant, but also be vague, and mislead just as humans do.

It is well known that path planning has always been an important study area for intelligent ships, especially for unmanned surface vehicles (USVs). Therefore, it is necessary to study the path-planning algorithm for USVs. As one of the basic algorithms for USV path planning, the rapidly-exploring random tree (RRT) is popular due to its simple structure, high speed and ease of modification. However, it also has some obvious drawbacks and problems. Designed to perfect defects of the basic RRT and improve the performance of USVs, an enhanced algorithm of path planning is proposed in this study, called the adaptive hybrid dynamic stepsize and target attractive force-RRT(AHDSTAF-RRT). The ability to pass through a narrow area and the forward speed in open areas of USVs are improved by adopting the AHDSTAF-RRT in comparison to the basic RRT algorithm. The improved algorithm is also applied to an actual gulf map for simulation experiments, and the experimental data is collected and organized. Simulation experiments show that the proposed AHDSTAF-RRT in this paper outperforms several existing RRT algorithms, both in terms of path length and calculating speed.