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.