Feedback is delighted to learn that researchers have discovered what Taylor Swift is accidentally doing to rescue the science of plants from mid-ness. We never miss a beat, so Feedback, prompted by assistant news editor and Swiftie Alexandra Thompson, has been taking a close look at a major paper in the Annals of Botany, published in August. It is called "Dance with plants: Taylor Swift's music videos as advance organizers for meaningful learning in botany" . The thesis is that high school students exhibit "a general low interest in plants", leading to "plant blindness". Teachers struggling to convey the magic of botany are repeating material and are getting sick of it.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Some things are sadly inevitable: death, taxes, another Coldplay album. One such inevitability, long since proved beyond any reasonable doubt, is that if you try to suppress an embarrassing story, you will only draw more attention to it. This phenomenon is called the Streisand Effect, after an incident in 2003 when Barbra Streisand sued to have an aerial photograph taken off the internet.

I wasn't around when the internet was discovered for the first time but I could only imagine this must be what it's like to do so. Feature Image was a prompt suggested by the AI itself, "A person's mind being stretched and expanded by the limitless possibilities of artificial intelligence". And the tl;dr above is written by it as well. Linh Dao Smooke is the wife of David Smooke, the founder of Hacker Noon. She is also a co-founder of the company and serves as the Chief Strategy Officer.

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I recently collaborated on several projects involving chatbots and had the opportunity to discuss with industry experts about the main difficulties that are often encountered in this type of project. While it is becoming easier and easier to build conversational assistants, it looks like there are some problems that emerge systematically as the chatbot grows, as a consequence of not having a proper intent architecture. In this article, I propose a way of designing intents with the goal of avoiding these bad symptoms. I'll deal primarily with chatbots whose input may be both free text or voice (and so intent classification is involved), and from multiple choice. The good news is that we can use both input modes in the same chatbot, using the best one on the right occasion.

This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.

Various conceptual and descriptive models of conversational search have been proposed in the literature -- while useful, they do not provide insights into how interaction between the agent and user would change in response to the costs and benefits of the different interactions. In this paper, we develop two economic models of conversational search based on patterns previously observed during conversational search sessions, which we refer to as: Feedback First where the agent asks clarifying questions then presents results, and Feedback After where the agent presents results, and then asks follow up questions. Our models show that the amount of feedback given/requested depends on its efficiency at improving the initial or subsequent query and the relative cost of providing said feedback. This theoretical framework for conversational search provides a number of insights that can be used to guide and inform the development of conversational search agents. However, empirical work is needed to estimate the parameters in order to make predictions specific to a given conversational search setting.

Drew McDermott Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans.

Another popular event at the DC was the student-mentor dinner, held this year at Elephant Walk, which provided an opportunity for students and researchers to interact in an informal setting. We report on the eleventh annual SIGART/AAAI Doctoral Consortium, held in conjunction with the National Conference on Artificial Intelligence (AAAI-06). We discuss highlights and innovations of this year's consortium and include pointers to the consortium website. At the DC, Ph.D. students in artificial intelligence presented their proposed research and received feedback from a panel of researchers and other students. The primary goal of the DC is to give students feedback on their proposed dissertation research at a critical time, by independent, knowledgeable reviewers external to their institutions.

In this article, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free-body diagrams into the system, just as they would with pencil and paper, but our system also checks the student's work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign more free-response questions, increasing teacher's knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply display memorized information.