To integrate robotics into society, it is first necessary to measure and analyze current societal responses to areas within robotics. This article is the second in a continuing series of reports on the societal effects of various aspects of robotics. In my previous article, I discussed the problems of sensor abuse and outlined a program of treatment. However, despite the wide dissemination of that article, there are still numerous empty beds at the Susan Calvin Clinic for the Prevention of Sensor Abuse. Sensor abuse continues unabated despite strong evidence that there is a better way.

With the growing importance of multiagent teamwork, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. To this end, we are creating isaac, a team analyst agent for post hoc, offline agent-team analysis. With the growing importance of teamwork, there is now a critical need for tools to help humans analyze, evaluate, and understand team behaviors. Indeed, in multiagent domains with tens or even hundreds of agents in teams, agent interactions are often highly complex and dynamic, making it difficult for human developers to analyze agent-team behaviors. The problem is further exacerbated in environments where agents are developed by different developers, where even the intended interactions are unpredictable.

When future robots enter the world, they won't have a learning curve. Artificial intelligence researchers are creating tools to help teach the robots that will assemble our gadgets in factories, or do chores around our home, before they ever step (or roll) into the real world. These simulators, most recently announced by Nvidia as a project called Isaac's Lab but also pioneered by Alphabet's DeepMind and Elon Musk's OpenAI, are 3D spaces that have physics just like reality, with virtual objects that act the same way as their physical counterparts. Virtual spaces are required because one way of teaching robots is a method called reinforcement learning, or the chore of doing one task over and over again until it's done correctly. In a simulation, training the bots can be done more quickly and cheaply than in real life because lots of simulated robots can learn at once.

SMEs cannot afford to ignore larger companies' use of artificial intelligence to solve customer needs, but nor should they rush to invest in imperfect computing. The best customer service agent is knowledgeable, always available and knows precisely what your customer wants, but increasingly, they're not human. Chatbots and other artificial intelligence tools could help your SME offer a more comprehensive, personalised customer service without hiring new staff. But, alongside the benefits of computerising customer service, there are risks, particularly as it's such early days for the technology involved. Chatbots let customers type queries – through messaging mediums such as Facebook or WhatsApp – and get an automated response without a human paid employee needing to intervene.

Artificial intelligence used to be something that featured in science fiction films. Following the debut in June of Sunspring, a hilariously disjointed short film written entirely by AI, a feature-length movie has been written by AI – with human input – in an effort to nail the formula for box office success. Disturbed by the death of her young daughter, a career-driven mother and her out-of-work husband leave the city and move to an idyllic and secluded country home. Funds are being raised to make Impossible Things on Kickstarter, where it is billed as "the first AI co-written feature film". Jack Zhang, the 24-year-old Canadian mathematician and software architect behind the project, used AI to analyse reams of movie data and identify the plot combinations audiences like.

With the growing importance of multiagent team-work, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. To this end, we are creating isaac, a team analyst agent for post hoc, offline agent-team analysis. ISAAC'S novelty stems from a key design constraint that arises in team analysis: Multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. These heterogeneous team models are automatically acquired by machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC uses multiple presentation techniques that can aid human understanding of the analyses. This article presents ISAAC'S general conceptual framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup Scientific Challenge Award.

To integrate robotics into society, it is first necessary to measure and analyze current societal responses to areas within robotics. This article is the second in a continuing series of reports on the societal effects of various aspects of robotics. In my previous article, I discussed the problems of sensor abuse and outlined a program of treatment. However, despite the wide dissemination of that article, there are still numerous empty beds at the Susan Calvin Clinic for the Prevention of Sensor Abuse. Sensor abuse continues unabated despite strong evidence that there is a better way. In this article, I explore the age-old question, Why does the robotics community look down on efficient sensing systems?