Robot systems capable of executing tasks based on language instructions have been actively researched. It is challenging to convey uncertain information that can only be determined on-site with a single language instruction to the robot. In this study, we propose a system that includes ambiguous parts as template variables in language instructions to communicate the information to be collected and the options to be presented to the robot for predictable uncertain events. This study implements prompt generation for each robot action function based on template variables to collect information, and a feedback system for presenting and selecting options based on template variables for user-to-robot communication. The effectiveness of the proposed system was demonstrated through its application to real-life support tasks performed by the robot.

Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.

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If you were not living in a cave during the last 2 or 3 years, you probably have heard about 5G. In many conversations I had, and in many articles, I've been reading during the last years, 5G, IoT, and AI has often been associated for many different reasons. And today, I want to explore this enjoyable technological cooperation. The next generation of mobile telephony is also a paradigm shift in our era. It will allow the use of devices and applications that only work through high data traffic.

A system that remembers customers' preferences, can understand speech and text and that learns the more it's used--that's the magic of machine learning. Machine learning is used to understand customers, drive personalization, streamline processes and create convenient and memorable customer experiences. Here are 20 examples of machine learning in action. Guests at Disney parks use MagicBand wristbands as room keys, tickets and payment. The wristband collects information of where the guests are in the park to recommend experiences and even route people around busy areas.

A system that remembers customers' preferences, can understand speech and text and that learns the more it's used--that's the magic of machine learning. Machine learning is used to understand customers, drive personalization, streamline processes and create convenient and memorable customer experiences. Here are 20 examples of machine learning in action. Guests at Disney parks use MagicBand wristbands as room keys, tickets and payment. The wristband collects information of where the guests are in the park to recommend experiences and even route people around busy areas.

In my last article, How to Use AI to Control Your Smart Home, I discussed the changes coming to residential automation with the introduction of AI and processing performed in the cloud. This is bringing advances to smart homes that were the dreams of science fiction only a few years ago. However, with great power comes great responsibility and there is a dark side to the power of AI in a home; privacy. Anyone watching the news is aware of the near-daily headline of privacy fiascos by major technology corporations. Unfortunately, some of these are the same corporations that are delivering a number of the most advanced AI products for smart homes.

A system that remembers customers' preferences, can understand speech and text and that learns the more it's used--that's the magic of machine learning. Machine learning is used to understand customers, drive personalization, streamline processes and create convenient and memorable customer experiences. Here are 20 examples of machine learning in action. Guests at Disney parks use MagicBand wristbands as room keys, tickets and payment. The wristband collects information of where the guests are in the park to recommend experiences and even route people around busy areas.

The end result of trusting technology we don't fully understand. Killer robots stalk the ruined landscape. And Arnold Schwarzenegger appears, in undoubtedly the easiest role of his career. What our minds jump to when we hear the words "machine learning" and "danger" together isn't prophetic (or even realistic). But popular culture has crammed our brains full of cautionary tales of AI gone bad. The most influential minds in science and technology warn of the same.

Republican Senator Jeff Flake of Arizona revealed on Wednesday that he intends to introduce a resolution to roll back privacy rules that restrict what internet service providers can do with customer data, according to a report from Politico. Flake said he intended to eliminate the Broadband Consumer Privacy Rules by using the Congressional Review Act, which allows Congress to undo rules set by federal agencies with a simple majority vote. The rules that Flake is taking aim at were first passed by the FCC in October 2016 and were aimed at giving consumers more control over their data by requiring internet service providers to ask for permission before using sensitive information. "We like to say it makes [consumers] the king or queen of their information," Dallas Harris, a policy fellow at open internet advocacy group Public Knowledge, told International Business Times. "The way the order does that is by saying, 'Okay internet service provider, if you would like to use sensitive information, you have to ask your consumers first.' The order defines sensitive information as information regarding a user's finances, health, information from children, precise geolocation data, web browsing history, and app usage history. It also includes any unencrypted message content that may be accessible to the service provider. Information deemed to be non-sensitive can be collected by default requires an option to opt-out. Internet service providers oppose the rules and lobbied for the FCC to undo them --a very real possibility under the leadership of Ajit Pai, who was appointed to the head of the FCC by President Donald Trump. Commissioner Pai opposed the rules when they were passed last year, arguing at the time, "if the FCC truly believes that these new rules are necessary to protect consumer privacy, then the government now must move forward to ensure uniform regulation of all companies in the Internet ecosystem at the new baseline the FCC has set." Pai's argument is essentially that if the FCC wants to make handing over consumer data require an opt-in for ISPs, it should for edge providers like Facebook and Google as well. It's unlikely Pai would extend the rules as he suggested, but he could modify them. "The agency is free to change its mind" on the ruling, Harris explained, "It just can't do so in an arbitrary and capricious manner...they can't just willy-nilly with no reason decide to rescind them." Doing so would require opening the rules up to public comment and would require the agency to change the rules based on new facts. Pai and the FCC also have the option to make changes by reviewing petitions for reconsideration, which allows the commission to change a rule without opening the process up to public comment. Both options are preferable to using the Congressional Review Act, which Harris calls "entirely too blunt of an instrument" to handle such a rule. "There really is no benefit to using the CRA here.