Developers often struggle to navigate an Open Source Software (OSS) project's issue-tracking system and find a suitable task. Proper issue labeling can aid task selection, but current tools are limited to classifying the issues according to their type (e.g., bug, question, good first issue, feature, etc.). In contrast, this paper presents a tool (GiveMeLabeledIssues) that mines project repositories and labels issues based on the skills required to solve them. We leverage the domain of the APIs involved in the solution (e.g., User Interface (UI), Test, Databases (DB), etc.) as a proxy for the required skills. GiveMeLabeledIssues facilitates matching developers' skills to tasks, reducing the burden on project maintainers. The tool obtained a precision of 83.9% when predicting the API domains involved in the issues. The replication package contains instructions on executing the tool and including new projects. A demo video is available at https://www.youtube.com/watch?v=ic2quUue7i8

The field of user experience (UX) based on the design philosophy of "user-centered design" is moving towards the intelligence era. Still, the existing UX paradigm mainly aims at non-intelligent systems and lacks a systematic approach to UX for intelligent systems. Throughout the development of UX, the UX paradigm shows the evolution characteristics of the cross-technology era. At present, the intelligence era has put forward new demands on the UX paradigm. For this reason, this paper proposes a "UX 3.0" paradigm framework and the corresponding UX methodology system in the intelligence era. The "UX 3.0" paradigm framework includes five categories of UX methods: ecological experience, innovation-enabled experience, AI-enabled experience, human-AI interaction-based experience, and human-AI collaboration-based experience methods, each providing corresponding multiple UX paradigmatic orientations. The proposal of the "UX 3.0" paradigm helps improve the existing UX methods and provides methodological support for the research and applications of UX in developing intelligent systems. Finally, this paper looks forward to future research and applications of the "UX 3.0" paradigm.

The following post is an excerpt from my book'Designing Human-Centric AI Experiences' on applied UX design for Artificial intelligence. We tend to anthropomorphize AI systems, i.e., we impute them with human-like qualities. Many popular depictions of AI, like Samantha in the movie Her or Ava in Ex-Machina, show a personality and sometimes even display emotions. Many AI systems like Alexa or Siri are designed with a personality in mind. However, choosing to give your AI system a personality has its advantages and disadvantages.

It has a broad range of general knowledge which it can tap into to have discussions on various topics. It is aimed more at helping users solve problems, make decisions and gain new insights. Dragonfly has a deeper level of domain-specific knowledge which allows it to reason about topics such as science, engineering and healthcare. Sage is tailored for simpler back-and-forth conversations. While Sage and Dragonfly have different strengths, they share some similarities as AI systems built by Anthropic to be helpful, harmless and honest using techniques like Constitutional AI. But they play quite different roles as virtual assistants focused on either conversation or problem-solving.

New powerful tools for tackling life science problems have been created by recent advances in machine learning. The purpose of the paper is to discuss the potential advantages of gene recommendation performed by artificial intelligence (AI). Indeed, gene recommendation engines try to solve this problem: if the user is interested in a set of genes, which other genes are likely to be related to the starting set and should be investigated? This task was solved with a custom deep learning recommendation engine, DeepProphet2 (DP2), which is freely available to researchers worldwide via https://www.generecommender.com?utm_source=DeepProphet2_paper&utm_medium=pdf. Hereafter, insights behind the algorithm and its practical applications are illustrated. The gene recommendation problem can be addressed by mapping the genes to a metric space where a distance can be defined to represent the real semantic distance between them. To achieve this objective a transformer-based model has been trained on a well-curated freely available paper corpus, PubMed. The paper describes multiple optimization procedures that were employed to obtain the best bias-variance trade-off, focusing on embedding size and network depth. In this context, the model's ability to discover sets of genes implicated in diseases and pathways was assessed through cross-validation. A simple assumption guided the procedure: the network had no direct knowledge of pathways and diseases but learned genes' similarities and the interactions among them. Moreover, to further investigate the space where the neural network represents genes, the dimensionality of the embedding was reduced, and the results were projected onto a human-comprehensible space. In conclusion, a set of use cases illustrates the algorithm's potential applications in a real word setting.

In recommendation systems, a large portion of the ratings are missing due to the selection biases, which is known as Missing Not At Random. The counterfactual inverse propensity scoring (IPS) was used to weight the imputation error of every observed rating. Although effective in multiple scenarios, we argue that the performance of IPS estimation is limited due to the uncertainty miscalibration of propensity estimation. In this paper, we propose the uncertainty calibration for the propensity estimation in recommendation systems with multiple representative uncertainty calibration techniques. Theoretical analysis on the bias and generalization bound shows the superiority of the calibrated IPS estimator over the uncalibrated one. Experimental results on the coat and yahoo datasets shows that the uncertainty calibration is improved and hence brings the better recommendation results.

Update: March 2, 2023 (3:22 AM AT): It appears that the issue has been fixed. Original article: February 28, 2023 (12:33 PM ET): If you tried to use Google Assistant this morning to do something like activate your lights or turn on your TV, you may have noticed something weird. It appears Google Assistant has forgotten how to control smart home devices.

ChatGPT has proven to be the ultimate wingman among men looking for love online - the chatbot helped one Tinder users get a date in less than one hour. Singles are harnessing the power of OpenAI's tool to curate the perfect dating profiles and responses to snag a potential match, as some feel dating apps have always favored women and ChatGPT is helping them'tip the scales.' Men are going from zero dates to dozens in just the first month of using the chatbot that creates whimsical poems, romantic notes and confident replies for individuals who would otherwise'struggle to come up with conversation starters.' While'the results have been astounding,' some people feel it is dishonest to use ChatGPT to reel women in because they are unaware they are talking to a chatbot. However, one Tinder user is not thinking twice about getting a little help to attract more women.

Artificial intelligence (AI) has become an increasingly important tool for businesses of all sizes. While AI was once seen as a technology reserved for large corporations with deep pockets, it has now become accessible even to small businesses. In fact, AI tools can be particularly beneficial for small businesses, helping them to automate routine tasks, improve efficiency, and provide better customer experiences. In this article, we'll explore why AI tools are essential for small businesses and how they can help businesses of all types and sizes. One of the primary benefits of AI tools is their ability to automate routine tasks.

In this paper, we present a demo of an intelligent personal agent called Hey Dona (or just Dona) with virtual voice assistance in student course registration. It is a deployed project in the theme of AI for education. In this digital age with a myriad of smart devices, users often delegate tasks to agents. While pointing and clicking supersedes the erstwhile command-typing, modern devices allow users to speak commands for agents to execute tasks, enhancing speed and convenience. In line with this progress, Dona is an intelligent agent catering to student needs by automated, voice-operated course registration, spanning a multitude of accents, entailing task planning optimization, with some language translation as needed. Dona accepts voice input by microphone (Bluetooth, wired microphone), converts human voice to computer understandable language, performs query processing as per user commands, connects with the Web to search for answers, models task dependencies, imbibes quality control, and conveys output by speaking to users as well as displaying text, thus enabling human-AI interaction by speech cum text. It is meant to work seamlessly on desktops, smartphones etc. and in indoor as well as outdoor settings. To the best of our knowledge, Dona is among the first of its kind as an intelligent personal agent for voice assistance in student course registration. Due to its ubiquitous access for educational needs, Dona directly impacts AI for education. It makes a broader impact on smart city characteristics of smart living and smart people due to its contributions to providing benefits for new ways of living and assisting 21st century education, respectively.