Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we look into bias in AI-based recruitment tools, find out about a new image dataset for ethical AI benchmarking, dig into human-robot interactions and social robotics, and look back on another busy year in the world of AI. We've been meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the European Conference on Artificial Intelligence (ECAI-2025) . In the second interview of the series, we caught up with Frida Hartman to find out how her PhD is going so far, and plans for the next steps in her investigations. Frida, along with co-authors Mario Mirabile and Michele Dusi, was also the winner of the ECAI-2025 Diversity & Inclusion Competition, for work entitled .

Prompting is not the same as natural language. When people converse with each other, they work together to communicate, forming mental models of a conversation partner's communicative intent based not only on words but also on paralinguistic and other contextual cues, theory-of-mind abilities, and by requesting clarification as needed.4 By contrast, while some prompts resemble natural language, many of the most "successful" prompts do not--for instance, image generation is a domain where arcane prompts tend to produce better results than those in plain language.1 Further, prompts are surprisingly sensitive to variations in wording, spelling, and punctuation in ways that lead to substantial changes in model outputs, whereas these same permutations would be unlikely to impact human interpretation of intent--for example, jailbreak prompts using suffix attacks10 or word-repetition commands.7 While some prompts resemble natural language, many of the most "successful" prompts do not.

The latest batch of rumors make it pretty clear that a PlayStation 5 Pro is coming this year, but will anyone really care about slightly better 4K graphics? This week, Engadget Senior Editor Jessica Conditt joins Cherlynn and Devindra to chat about the PS5 Pro, as well as her piece on the PlayDate two years after its release. You could say the Playdate is pretty much the opposite of another expensive high-end console. In other news, we discuss the death of Boston Dynamic's hydraulic Atlas robot, and the birth of an all-new digital model. We also chat about the abrupt closure of Possibility Space, an ambitious indie game studio.

Temporal graph representation learning (TGRL) is crucial for modeling complex, dynamic systems in real-world networks. Traditional TGRL methods, though effective, suffer from high computational demands and inference latency. This is mainly induced by their inefficient sampling of temporal neighbors by backtracking the interaction history of each node when making model inference. This paper introduces a novel efficient TGRL framework, No-Looking-Back (NLB). NLB employs a "forward recent sampling" strategy, which bypasses the need for backtracking historical interactions. This strategy is implemented using a GPU-executable size-constrained hash table for each node, recording down-sampled recent interactions, which enables rapid response to queries with minimal inference latency. The maintenance of this hash table is highly efficient, with $O(1)$ complexity. NLB is fully compatible with GPU processing, maximizing programmability, parallelism, and power efficiency. Empirical evaluations demonstrate that NLB matches or surpasses state-of-the-art methods in accuracy for link prediction and node classification across six real-world datasets. Significantly, it is 1.32-4.40 $\times$ faster in training, 1.2-7.94 $\times$ more energy efficient, and 1.97-5.02 $\times$ more effective in reducing inference latency compared to the most competitive baselines. The link to the code: https://github.com/Graph-COM/NLB.

Understanding and predicting the emotional trajectory in multi-party multi-turn conversations is of great significance. Such information can be used, for example, to generate empathetic response in human-machine interaction or to inform models of pre-emptive toxicity detection. In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling. These modeling dimensions are then incorporated into two deep neural network architectures, a sequence model and a graph convolutional network model. The former is designed to capture the sequence of utterances in a dialogue, while the latter captures the sequence of utterances and the network formation of multi-party dialogues. We perform a comprehensive empirical evaluation of the various proposed models for addressing the PEC problem. The results indicate (i) the importance of the self-dependency and recency model dimensions for the prediction task, (ii) the quality of simpler sequence models in short dialogues, (iii) the importance of the graph neural models in improving the predictions in long dialogues.

AI (Artificial Intelligence) won't just start appearing one day like an all-knowing computer Genie in a lamp-shaped cloud, but you'll be surprised and amazed at how it is currently and will continue to surface in subtle ways that will change many things including entire industries and how you buy their products and services. Some things we have to purchase to survive in the modern world take research, study, and comparison and are generally hard to get good, accurate, and relevant information on so we end up picking arbitrarily or by copying what people we know did. I'm looking at your auto insurance, cell service, automobiles, and computers to name a few. AI won't be one big thing in our lives, it will be thousands of little things. They won't usually manifest themselves in an all-powerful central role like Alexa or Siri, they will be an invisible army of nameless extras hardly noticeable in the background and yet essential to almost every scene of our lives.

Significant advances in AI have also enabled Microsoft to bring new capabilities to customers through our products and services, including GitHub Copilot, an AI pair programmer capable of turning natural language prompts into code, and a preview of Microsoft Designer, a graphic design app that supports the creation of social media posts, invitations, posters, and one-of-a-kind images. These offerings provide an early glimpse of how new AI capabilities, such as large language models, can enable people to interact with machines in increasingly powerful ways. They build on a significant, long-term commitment to fundamental research in computing and across the sciences, and the research community at Microsoft plays an integral role in advancing the state of the art in AI, while working closely with engineering teams and other partners to transform that progress into tangible benefits. In 2022, Microsoft Research established AI4Science, a global organization applying the latest advances in AI and machine learning toward fundamentally transforming science; added to and expanded the capabilities of the company's family of foundation models; worked to make these models and technologies more adaptable, collaborative, and efficient; further developed approaches to ensure that AI is used responsibly and in alignment with human needs; and pursued different approaches to AI, such as causal machine learning and reinforcement learning. We shared our advances across AI and many other disciplines during our second annual Microsoft Research Summit, where members of our research community gathered virtually with their counterparts across industry and academia to discuss how emerging technologies are being explored and deployed to bring the greatest possible benefits to humanity.

… cloud in all things related to analytics, AI, machine learning (ML), and other areas. … Artificial intelligence made great inroads in 2022.

Another Mars robot is settling in for a long, long sleep. With dust caking its solar panels, InSight has been losing the ability to recharge for months -- in the spring, it was operating at just one-tenth of its landing power. Now the thick layers of dust might have doomed InSight for good. NASA announced on December 19 that its InSight lander had not responded to communications from Earth, and "it's assumed InSight may have reached its end of operations." InSight, short for Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport, landed on Mars on November 26, 2018.

As the end of 2022 draws near, it is time once again for Creative Virtual's annual year in review blog post. Every year we take this opportunity to reflect on the hard work of our team, our contributions to the conversational AI industry, and a few of our company's biggest highlights from the past 12 months. Two of the things we are proudest of at Creative Virtual are our experienced, dedicated team and the unique expertise we provide to our customers and partners. Whether it's through our product development or our collaborations with individual clients, it's important to us that we consistently deliver the best solutions possible. Having an analyst group recognise us for this is always an exciting bonus – and that's what happened again this year.