It's all happening at once: chat bots, chat bots, and more chat bots. Now that Facebook is going to add bots to Messenger, there's no turning back. We'll buy flowers with bots, we'll order car service with bots, we'll plan vacations with bots. We'll engage with more brands, more effectively, all with bots. Just so there's no misunderstanding, this early wave of "bots" will have very little to do with complex artificial intelligence.

The 15th International Conference on AI and Law (ICAIL 2015) will be held in San Diego, California, USA, June 8-12, 2015, at the University of San Diego, at the Kroc Institute, under the auspices of the International Association for Artificial Intelligence and Law (IAAIL), an organization devoted to promoting research and development in the field of AI and law with members throughout the world. The conference is held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and with ACM SIGAI (the Special Interest Group on Artificial Intelligence of the Association for Computing Machinery).

We review the First International Competition on Computational Models of Argumentation (ICMMA'15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

If the artificial intelligence research community is to have a challenge problem as an incentive for research, as many have called for, it behooves us to learn the principles of past successful inducement prize competitions. Those principles argue against the Turing test proper as an appropriate task, despite its appropriateness as a criterion (perhaps the only one) for attributing intelligence to a machine.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The 9th International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5.

As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high-school student. For the fourth grade level questions, I argue that questions that require the understanding of time, impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high-school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs.

This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way.

Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that machine performance on standardized tests should be a key component of any new measure of AI, because attaining a high level of performance requires solving significant AI problems involving language understanding and world modeling - critical skills for any machine that lays claim to intelligence. In addition, standardized tests have all the basic requirements of a practical test: they are accessible, easily comprehensible, clearly measurable, and offer a graduated progression from simple tasks to those requiring deep understanding of the world.

We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

Keywords: 5-minute interview apple cypher express.js Bryce Merkl Sasaki is the Community Content Manager for Neo Technology. He studied professional and creative writing for undergrad and has been freelancing for 7 years. Recently, he worked at an inbound marketing agency in Philadelphia as a copywriter before moving to California. When not working, he likes to spend his time working on his novel, looking for pickup soccer games and reading voraciously.