Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems. In real-world applications, analysts often employ several algorithms concurrently and compare their solution sets to gain insight into the characteristics of different algorithms and explore a broader range of feasible solutions. However, EMO algorithms are typically treated as black boxes, leading to difficulties in performing detailed analysis and comparisons between the internal evolutionary processes. Inspired by the successful application of visual analytics tools in explainable AI, we argue that interactive visualization can significantly enhance the comparative analysis between multiple EMO algorithms. In this paper, we present a visual analytics framework that enables the exploration and comparison of evolutionary processes in EMO algorithms. Guided by a literature review and expert interviews, the proposed framework addresses various analytical tasks and establishes a multi-faceted visualization design to support the comparative analysis of intermediate generations in the evolution as well as solution sets. We demonstrate the effectiveness of our framework through case studies on benchmarking and real-world multi-objective optimization problems to elucidate how analysts can leverage our framework to inspect and compare diverse algorithms.

GPT-4 was released in March 2023 to wide acclaim, marking a very substantial improvement across the board over GPT-3.5 (OpenAI's previously best model, which had powered the initial release of ChatGPT). However, despite the genuinely impressive improvement, there are good reasons to be highly skeptical of GPT-4's ability to reason. This position paper discusses the nature of reasoning; criticizes the current formulation of reasoning problems in the NLP community, as well as the way in which LLM reasoning performance is currently evaluated; introduces a small collection of 21 diverse reasoning problems; and performs a detailed qualitative evaluation of GPT-4's performance on those problems. Based on this analysis, the paper concludes that, despite its occasional flashes of analytical brilliance, GPT-4 at present is utterly incapable of reasoning.

Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn chatbot interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue multiple interconnected learning objectives, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the limited history maintained for the conversation and the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.

With ChatGPT and other artificial intelligence tools mom and dad can now access massive amounts of parenting knowledge at their fingertips. A lawsuit has been filed against an Atlanta-area hospital after a baby was allegedly decapitated as his mother was giving birth last month. Jessica Ross, 20, went to the emergency room at the Southern Regional Medical Center in Riverdale on July 9, after her water broke at around 10 a.m., FOX Atlanta reported. At 8:40 p.m., she was fully dilated and began pushing. The baby stopped descending because of shoulder dystocia while being delivered vaginally, meaning the baby's shoulders could not fit through the pelvic area, the lawsuit said.

The programme of the International Conference on Machine Learning (ICML) featured an invited talk by Jennifer Doudna entitled "The future of ML in biology: CRISPR for health and climate". Jennifer Doudna and Emmanuelle Charpentier won the 2020 Nobel Prize in Chemistry for "the development of a method for genome editing". The method in question is often referred to as CRISPR/Cas9 genetic scissors. Using this technique, researchers can change the DNA of animals, plants and microorganisms with extremely high precision. This technology has already had a huge impact on the biological sciences.

Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user's perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flossing habits to a chatbot that either, (1-None): did not explicitly reference previous user utterances, (2-Verbatim): referenced previous utterances verbatim, or (3-Paraphrase): used paraphrases to reference previous utterances. Participants perceived Verbatim and Paraphrase chatbots as more intelligent and engaging. However, the Verbatim chatbot also raised privacy concerns with participants. To gain insights as to why people prefer certain conditions or had privacy concerns, we conducted semi-structured interviews with 15 participants. We discuss implications from our findings that can help designers choose an appropriate format to reference previous user utterances and inform in the design of longitudinal dialogue scripting.

Figure 1: AttentionViz, our interactive visualization tool, allows users to explore transformer self-attention at scale by creating a joint embedding space for queries and keys. Each point in the scatterplot represents the query or key version of a word, as denoted by point color. Users can explore individual attention heads (left) or zoom out for a "global" view of attention (right). Abstract--Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind our method is to visualize a joint embedding of the query and key vectors used by transformer models to compute attention. Unlike previous attention visualization techniques, our approach enables the analysis of global patterns across multiple input sequences. We create an interactive visualization tool, AttentionViz (demo: http://attentionviz.com), based on these joint query-key embeddings, and use it to study attention mechanisms in both language and vision transformers. We demonstrate the utility of our approach in improving model understanding and offering new insights about query-key interactions through several application scenarios and expert feedback. The transformer neural network architecture [52] is having a major impact In this work, we describe a new visualization technique aimed at on fields ranging from natural language processing (NLP) [13, 42] better comprehending how transformers operate. Indeed, transformers are now deployed in introduction to transformers in Sec. However, the mechanisms these models to learn and use a rich set of relationships between input behind this success remain somewhat mysterious, especially as elements.

In 1950, the British computer scientist Alan Turing disputed whether human reasoning can be matched by computers: "Can machines think?" (TURING, 1950). Subsequently, he proposed the Turing Test to measure computer or artificial intelligence. In a Turing test, a human interrogator is presented with responses from a human and a computer (with the ability to generate written texts in real-time). If the interrogator cannot distinguish between the answers, the computer system passes the Turing Test. Although several computer programs and chatbots like Eliza demonstrated success in the Turing test ((Weizenbaum, 1966) (Güzeldere & Franchi, 1995)), these programs arguably used certain tricks to pass the test (Pinar Saygin et al., 2000) rather than demonstrating any significant intelligence. With the advancement in machine learning and natural language processing (NLP), chatbots have gained significant research attention and have been used for a variety of commercial and non-commercial applications ((Luo et al., 2022), (Adamopoulou & Moussiades, 2020), (Ranoliya et al., 2017), (Rahman et al., 2017), (Zhou et al., 2020)). Despite their vast adoption, most chatbots do not have personalization, and user satisfaction remains questionable (Følstad & Brandtzaeg, 2020). This limitation prompted researchers and developers to focus on chatbot engagement in making chatbots more conversational.

Peter Paccone, a social studies teacher in San Marino, Calif., has a new teacher's aid helping him in the classroom this year. He plans to defer to his helper to explain some simpler topics to his class of high schoolers, like the technical aspects of how a cotton gin worked, in order to free up time for him to discuss more analytical concepts, like the effects of the first industrial revolution. "What I feel that I don't have to do any longer is cover all the content," Paccone told a group of more than 40 educators in a May Zoom workshop, which he organized. If artificial intelligence is on the cusp of reshaping entire aspects of our society--from healthcare to warfare--the first realm that leaps to many minds is education: Asked a question online, the ChatGPT chatbot will produce an answer that reads like an essay. So as students and teachers prepare for a new school year, they are also grappling with AI's implications for learning, homework, and integrity.

I thought my interview with Grimes--the mysterious techno artist, fan of all nerddom, and the deepest of insiders in Elon Musk's world--would be one-on-one. Instead it wound up as a roundtable discussion. Turns out there are multiple personas embedded in the surprisingly haimish human who sat under a tree with me and spent the waning hours of an afternoon in conversation. There was Claire Boucher, the given name of a Vancouver kid obsessed with video games and devoted to provoking adults with misbehavior and the embrace of taboo subjects. There was Grimes, the self-invented, scrappy DIY musician and provocateur who weaves sci-fi into her work and released what Pitchfork judged to be the second-best song of the 2010s.